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Biblioteca

MARC Lista (tabellare)
The ACM journal of experimental algorithmics
The ACM journal of experimental algorithmics
Pubbl/distr/stampa New York, : ACM
Disciplina 004
Soggetto topico Computer algorithms
Data structures (Computer science)
Algorithms
Algorismes
Algorismes computacionals
Estructures de dades (Informàtica)
Soggetto genere / forma Periodicals.
Revistes electròniques
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Journal of experimental algorithmics
JEA
ACM JEA
Association for Computing Machinery journal of experimental algorithmics
Record Nr. UNINA-9910376057303321
New York, : ACM
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes
Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes
Autore Erciyes Kayhan
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (229 pages)
Disciplina 511.5
Collana Undergraduate Topics in Computer Science
Soggetto topico Computer science
Computer science - Mathematics
Discrete mathematics
Theory of Computation
Discrete Mathematics in Computer Science
Mathematical Applications in Computer Science
Python (Llenguatge de programació)
Algorismes
Àlgebra
Soggetto genere / forma Llibres electrònics
ISBN 9783030878863
9783030878856
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. Graphs, Matrices and Matroids -- 3. Parallel Matrix Algorithm Kernel -- 4. Basic Graph Algorithms -- 5. Connectivity, Matching and Matroids -- 6. Subgraph Search -- 7. Analysis of Large Graphs -- 8. Clustering in Complex Networks -- 9. Kronecker Graphs -- 10. Sample Algorithms for Complex Networks.
Record Nr. UNINA-9910510574003321
Erciyes Kayhan  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
Autore Souravlias Dimitris
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xiv, 92 pages) : illustrations
Disciplina 518.1
Collana SpringerBriefs in Optimization
Soggetto topico Operations research
Management science
Algorithms
Microprogramming
Discrete mathematics
Operations Research, Management Science
Control Structures and Microprogramming
Discrete Mathematics
Algorismes
Optimització matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68514-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References.
Record Nr. UNISA-996466562303316
Souravlias Dimitris  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Algorithm Portfolios : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
Algorithm Portfolios : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
Autore Souravlias Dimitris
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xiv, 92 pages) : illustrations
Disciplina 518.1
Collana SpringerBriefs in Optimization
Soggetto topico Operations research
Management science
Algorithms
Microprogramming
Discrete mathematics
Operations Research, Management Science
Control Structures and Microprogramming
Discrete Mathematics
Algorismes
Optimització matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68514-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References.
Record Nr. UNINA-9910484845003321
Souravlias Dimitris  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
Autore Vovk Vladimir <1960->
Edizione [2nd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (490 pages)
Disciplina 518.1
Soggetto topico Algorithms
Algorithms - Study and teaching
Teoria de la predicció
Algorismes
Processos estocàstics
Soggetto genere / forma Llibres electrònics
ISBN 3-031-06649-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning -- 1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness -- 1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors -- 2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors.
2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR -- 2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction -- 2.9.5 Examples of Nonconformity Measures -- 2.9.6 Kernel Methods -- 2.9.7 Burnaev-Wasserman Programme -- 2.9.8 Completeness Results -- 3 Conformal Prediction: Classification and General Case -- 3.1 Criteria of Efficiency for Conformal Prediction -- 3.1.1 Basic Criteria -- 3.1.2 Other Prior Criteria -- 3.1.3 Observed Criteria -- 3.1.4 Idealised Setting -- 3.1.5 Conditionally Proper Criteria of Efficiency -- 3.1.6 Criteria of Efficiency that Are not Conditionally Proper -- 3.1.7 Discussion -- 3.2 More Ways of Computing Nonconformity Scores -- 3.2.1 Nonconformity Scores from Nearest Neighbours -- 3.2.2 Nonconformity Scores from Support Vector Machines -- 3.2.3 Reducing Classification Problems to the Binary Case -- 3.3 Weak Teachers -- 3.3.1 Imperfectly Taught Predictors -- 3.3.2 Weak Validity.
3.3.3 Strong Validity -- 3.3.4 Iterated Logarithm Validity -- 3.3.5 Efficiency -- 3.4 Proofs -- 3.4.1 Proofs for Sect.3.1 -- Proof of Theorem 3.1 -- Proof of Theorem 3.2 -- Proof of Theorem 3.3 -- Proof of Theorem 3.4 -- 3.4.2 Proofs for Sect.3.3 -- Proof of Theorem 3.7, Part I -- Proof of Theorem 3.7, Part II -- Proof of Theorem 3.9 -- Proof of Theorem 3.13 -- 3.5 Context -- 3.5.1 Criteria of Efficiency -- 3.5.2 Examples of Nonconformity Measures -- 3.5.3 Universal Predictors -- 3.5.4 Weak Teachers -- 4 Modifications of Conformal Predictors -- 4.1 The Topics of This Chapter -- 4.2 Inductive Conformal Predictors -- 4.2.1 Inductive Conformal Predictors in the Online Mode -- 4.2.2 Inductive Conformal Predictors in the Offline and Semi-Online Modes -- 4.2.3 The General Scheme for Defining Nonconformity -- 4.2.4 Normalization and Hyper-Parameter Selection -- 4.3 Further Ways of Computing Nonconformity Scores -- 4.3.1 Nonconformity Measures Considered Earlier -- 4.3.2 De-Bayesing -- 4.3.3 Neural Networks and Other Multiclass Scoring Classifiers -- 4.3.4 Decision Trees and Random Forests -- 4.3.5 Binary Scoring Classifiers -- 4.3.6 Logistic Regression -- 4.3.7 Regression and Bootstrap -- 4.3.8 Training Inductive Conformal Predictors -- 4.4 Cross-Conformal Prediction -- 4.4.1 Definition of Cross-Conformal Predictors -- 4.4.2 Computational Efficiency -- 4.4.3 Validity and Lack Thereof for Cross-Conformal Predictors -- 4.5 Transductive Conformal Predictors -- 4.5.1 Definition -- 4.5.2 Validity -- 4.6 Conditional Conformal Predictors -- 4.6.1 One-Off Conditional Conformal Predictors -- 4.6.2 Mondrian Conformal Predictors and Transducers -- 4.6.3 Using Mondrian Conformal Transducers for Prediction -- 4.6.4 Generality of Mondrian Taxonomies -- 4.6.5 Conformal Prediction -- 4.6.6 Inductive Conformal Prediction -- 4.6.7 Label-Conditional Conformal Prediction.
4.6.8 Object-Conditional Conformal Prediction -- 4.7 Training-Conditional Validity -- 4.7.1 Conditional Validity -- 4.7.2 Training-Conditional Validity of Inductive Conformal Predictors -- 4.8 Context -- 4.8.1 Computationally Efficient Hedged Prediction -- 4.8.2 Specific Learning Algorithms and Nonconformity Measures -- 4.8.3 Training Conformal Predictors -- 4.8.4 Cross-Conformal Predictors and Alternative Approaches -- 4.8.5 Transductive Conformal Predictors -- 4.8.6 Conditional Conformal Predictors -- Part II Probabilistic Prediction -- 5 Impossibility Results -- 5.1 Introduction -- 5.2 Diverse Datasets -- 5.3 Impossibility of Estimation of Probabilities -- 5.3.1 Binary Case -- 5.3.2 Multiclass Case -- 5.4 Proof of Theorem 5.2 -- 5.4.1 Probability Estimators and Statistical Tests -- 5.4.2 Complete Statistical Tests -- 5.4.3 Restatement of the Theorem in Terms of Statistical Tests -- 5.4.4 The Proof of the Theorem -- 5.5 Context -- 5.5.1 More Advanced Results -- 5.5.2 Density Estimation, Regression Estimation, and Regression with Deterministic Objects -- 5.5.3 Universal Probabilistic Predictors -- 5.5.4 Algorithmic Randomness Perspective -- 6 Probabilistic Classification: Venn Predictors -- 6.1 Introduction -- 6.2 Venn Predictors -- 6.2.1 Validity of One-Off Venn Predictors -- 6.2.2 Are There Other Ways to Achieve Perfect Calibration? -- 6.2.3 Venn Prediction with Binary Labels and No Objects -- 6.3 A Universal Venn Predictor -- 6.4 Venn-Abers Predictors -- 6.4.1 Full Venn-Abers Predictors -- 6.4.2 Inductive Venn-Abers Predictors -- 6.4.3 Probabilistic Predictors Derived from Venn Predictors -- 6.4.4 Cross Venn-Abers Predictors -- 6.4.5 Merging Multiprobability Predictions into a Probabilistic Prediction -- 6.5 Proofs -- 6.5.1 Proof of Theorem 6.4 -- 6.5.2 PAVA and the Proof of Lemma 6.6 -- 6.5.3 Proof of Proposition 6.7 -- 6.6 Context.
6.6.1 Risk and Uncertainty -- 6.6.2 John Venn, Frequentist Probability, and the Problem of the Reference Class -- 6.6.3 Online Venn Predictors Are Calibrated -- 6.6.4 Isotonic Regression -- 7 Probabilistic Regression: Conformal Predictive Systems -- 7.1 Introduction -- 7.2 Conformal Predictive Systems -- 7.2.1 Basic Definitions -- 7.2.2 Properties of Validity -- 7.2.3 Simplest Example: Monotonic Conformity Measures -- 7.2.4 Criterion of Being a CPS -- 7.3 Least Squares Prediction Machine -- 7.3.1 Three Kinds of LSPM -- 7.3.2 The Studentized LSPM in an Explicit Form -- 7.3.3 The Offline Version of the Studentized LSPM -- 7.3.4 The Ordinary LSPM -- 7.3.5 Asymptotic Efficiency of the LSPM -- 7.3.6 Illustrations -- 7.4 Kernel Ridge Regression Prediction Machine -- 7.4.1 Explicit Forms of the KRRPM -- 7.4.2 Limitation of the KRRPM -- 7.5 Nearest Neighbours Prediction Machine -- 7.6 Universal Conformal Predictive Systems -- 7.6.1 Definitions -- 7.6.2 Universal Conformal Predictive Systems -- 7.6.3 Universal Deterministic Predictive Systems -- 7.7 Applications to Decision Making -- 7.7.1 A Standard Problem of Decision Making -- 7.7.2 Examples -- 7.7.3 Asymptotically Efficient Decision Making -- 7.7.4 Dangers of Overfitting -- 7.8 Computationally Efficient Versions -- 7.8.1 Inductive Conformal Predictive Systems -- 7.8.2 Cross-Conformal Predictive Distributions -- 7.8.3 Practical Aspects -- 7.8.4 Beyond Randomness -- 7.9 Proofs and Calculations -- 7.9.1 Proofs for Sect.7.2 -- Proof of Lemma 7.1 -- Proof of Proposition 7.2 -- 7.9.2 Proofs for Sect.7.3 -- Proof of Proposition 7.4 -- Proof of Proposition 7.5 -- Proof of Proposition 7.6 -- Proof of Proposition 7.7 -- Proof of Proposition 7.8 -- Computations for the Studentized LSPM -- The Ordinary LSPM -- Proof of (7.22) -- 7.9.3 Proof of Theorem 7.16 -- 7.9.4 Proofs for Sect.7.8 -- Proof of Proposition 7.17.
Proof of Proposition 7.18.
Record Nr. UNISA-996503551103316
Vovk Vladimir <1960->  
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
Autore Vovk Vladimir <1960->
Edizione [2nd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (490 pages)
Disciplina 518.1
Soggetto topico Algorithms
Algorithms - Study and teaching
Teoria de la predicció
Algorismes
Processos estocàstics
Soggetto genere / forma Llibres electrònics
ISBN 3-031-06649-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning -- 1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness -- 1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors -- 2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors.
2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR -- 2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction -- 2.9.5 Examples of Nonconformity Measures -- 2.9.6 Kernel Methods -- 2.9.7 Burnaev-Wasserman Programme -- 2.9.8 Completeness Results -- 3 Conformal Prediction: Classification and General Case -- 3.1 Criteria of Efficiency for Conformal Prediction -- 3.1.1 Basic Criteria -- 3.1.2 Other Prior Criteria -- 3.1.3 Observed Criteria -- 3.1.4 Idealised Setting -- 3.1.5 Conditionally Proper Criteria of Efficiency -- 3.1.6 Criteria of Efficiency that Are not Conditionally Proper -- 3.1.7 Discussion -- 3.2 More Ways of Computing Nonconformity Scores -- 3.2.1 Nonconformity Scores from Nearest Neighbours -- 3.2.2 Nonconformity Scores from Support Vector Machines -- 3.2.3 Reducing Classification Problems to the Binary Case -- 3.3 Weak Teachers -- 3.3.1 Imperfectly Taught Predictors -- 3.3.2 Weak Validity.
3.3.3 Strong Validity -- 3.3.4 Iterated Logarithm Validity -- 3.3.5 Efficiency -- 3.4 Proofs -- 3.4.1 Proofs for Sect.3.1 -- Proof of Theorem 3.1 -- Proof of Theorem 3.2 -- Proof of Theorem 3.3 -- Proof of Theorem 3.4 -- 3.4.2 Proofs for Sect.3.3 -- Proof of Theorem 3.7, Part I -- Proof of Theorem 3.7, Part II -- Proof of Theorem 3.9 -- Proof of Theorem 3.13 -- 3.5 Context -- 3.5.1 Criteria of Efficiency -- 3.5.2 Examples of Nonconformity Measures -- 3.5.3 Universal Predictors -- 3.5.4 Weak Teachers -- 4 Modifications of Conformal Predictors -- 4.1 The Topics of This Chapter -- 4.2 Inductive Conformal Predictors -- 4.2.1 Inductive Conformal Predictors in the Online Mode -- 4.2.2 Inductive Conformal Predictors in the Offline and Semi-Online Modes -- 4.2.3 The General Scheme for Defining Nonconformity -- 4.2.4 Normalization and Hyper-Parameter Selection -- 4.3 Further Ways of Computing Nonconformity Scores -- 4.3.1 Nonconformity Measures Considered Earlier -- 4.3.2 De-Bayesing -- 4.3.3 Neural Networks and Other Multiclass Scoring Classifiers -- 4.3.4 Decision Trees and Random Forests -- 4.3.5 Binary Scoring Classifiers -- 4.3.6 Logistic Regression -- 4.3.7 Regression and Bootstrap -- 4.3.8 Training Inductive Conformal Predictors -- 4.4 Cross-Conformal Prediction -- 4.4.1 Definition of Cross-Conformal Predictors -- 4.4.2 Computational Efficiency -- 4.4.3 Validity and Lack Thereof for Cross-Conformal Predictors -- 4.5 Transductive Conformal Predictors -- 4.5.1 Definition -- 4.5.2 Validity -- 4.6 Conditional Conformal Predictors -- 4.6.1 One-Off Conditional Conformal Predictors -- 4.6.2 Mondrian Conformal Predictors and Transducers -- 4.6.3 Using Mondrian Conformal Transducers for Prediction -- 4.6.4 Generality of Mondrian Taxonomies -- 4.6.5 Conformal Prediction -- 4.6.6 Inductive Conformal Prediction -- 4.6.7 Label-Conditional Conformal Prediction.
4.6.8 Object-Conditional Conformal Prediction -- 4.7 Training-Conditional Validity -- 4.7.1 Conditional Validity -- 4.7.2 Training-Conditional Validity of Inductive Conformal Predictors -- 4.8 Context -- 4.8.1 Computationally Efficient Hedged Prediction -- 4.8.2 Specific Learning Algorithms and Nonconformity Measures -- 4.8.3 Training Conformal Predictors -- 4.8.4 Cross-Conformal Predictors and Alternative Approaches -- 4.8.5 Transductive Conformal Predictors -- 4.8.6 Conditional Conformal Predictors -- Part II Probabilistic Prediction -- 5 Impossibility Results -- 5.1 Introduction -- 5.2 Diverse Datasets -- 5.3 Impossibility of Estimation of Probabilities -- 5.3.1 Binary Case -- 5.3.2 Multiclass Case -- 5.4 Proof of Theorem 5.2 -- 5.4.1 Probability Estimators and Statistical Tests -- 5.4.2 Complete Statistical Tests -- 5.4.3 Restatement of the Theorem in Terms of Statistical Tests -- 5.4.4 The Proof of the Theorem -- 5.5 Context -- 5.5.1 More Advanced Results -- 5.5.2 Density Estimation, Regression Estimation, and Regression with Deterministic Objects -- 5.5.3 Universal Probabilistic Predictors -- 5.5.4 Algorithmic Randomness Perspective -- 6 Probabilistic Classification: Venn Predictors -- 6.1 Introduction -- 6.2 Venn Predictors -- 6.2.1 Validity of One-Off Venn Predictors -- 6.2.2 Are There Other Ways to Achieve Perfect Calibration? -- 6.2.3 Venn Prediction with Binary Labels and No Objects -- 6.3 A Universal Venn Predictor -- 6.4 Venn-Abers Predictors -- 6.4.1 Full Venn-Abers Predictors -- 6.4.2 Inductive Venn-Abers Predictors -- 6.4.3 Probabilistic Predictors Derived from Venn Predictors -- 6.4.4 Cross Venn-Abers Predictors -- 6.4.5 Merging Multiprobability Predictions into a Probabilistic Prediction -- 6.5 Proofs -- 6.5.1 Proof of Theorem 6.4 -- 6.5.2 PAVA and the Proof of Lemma 6.6 -- 6.5.3 Proof of Proposition 6.7 -- 6.6 Context.
6.6.1 Risk and Uncertainty -- 6.6.2 John Venn, Frequentist Probability, and the Problem of the Reference Class -- 6.6.3 Online Venn Predictors Are Calibrated -- 6.6.4 Isotonic Regression -- 7 Probabilistic Regression: Conformal Predictive Systems -- 7.1 Introduction -- 7.2 Conformal Predictive Systems -- 7.2.1 Basic Definitions -- 7.2.2 Properties of Validity -- 7.2.3 Simplest Example: Monotonic Conformity Measures -- 7.2.4 Criterion of Being a CPS -- 7.3 Least Squares Prediction Machine -- 7.3.1 Three Kinds of LSPM -- 7.3.2 The Studentized LSPM in an Explicit Form -- 7.3.3 The Offline Version of the Studentized LSPM -- 7.3.4 The Ordinary LSPM -- 7.3.5 Asymptotic Efficiency of the LSPM -- 7.3.6 Illustrations -- 7.4 Kernel Ridge Regression Prediction Machine -- 7.4.1 Explicit Forms of the KRRPM -- 7.4.2 Limitation of the KRRPM -- 7.5 Nearest Neighbours Prediction Machine -- 7.6 Universal Conformal Predictive Systems -- 7.6.1 Definitions -- 7.6.2 Universal Conformal Predictive Systems -- 7.6.3 Universal Deterministic Predictive Systems -- 7.7 Applications to Decision Making -- 7.7.1 A Standard Problem of Decision Making -- 7.7.2 Examples -- 7.7.3 Asymptotically Efficient Decision Making -- 7.7.4 Dangers of Overfitting -- 7.8 Computationally Efficient Versions -- 7.8.1 Inductive Conformal Predictive Systems -- 7.8.2 Cross-Conformal Predictive Distributions -- 7.8.3 Practical Aspects -- 7.8.4 Beyond Randomness -- 7.9 Proofs and Calculations -- 7.9.1 Proofs for Sect.7.2 -- Proof of Lemma 7.1 -- Proof of Proposition 7.2 -- 7.9.2 Proofs for Sect.7.3 -- Proof of Proposition 7.4 -- Proof of Proposition 7.5 -- Proof of Proposition 7.6 -- Proof of Proposition 7.7 -- Proof of Proposition 7.8 -- Computations for the Studentized LSPM -- The Ordinary LSPM -- Proof of (7.22) -- 7.9.3 Proof of Theorem 7.16 -- 7.9.4 Proofs for Sect.7.8 -- Proof of Proposition 7.17.
Proof of Proposition 7.18.
Record Nr. UNINA-9910635392203321
Vovk Vladimir <1960->  
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algorithms and Complexity : 12th International Conference, CIAC 2021, Virtual Event, May 10–12, 2021, Proceedings / / edited by Tiziana Calamoneri, Federico Corò
Algorithms and Complexity : 12th International Conference, CIAC 2021, Virtual Event, May 10–12, 2021, Proceedings / / edited by Tiziana Calamoneri, Federico Corò
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XI, 410 p. 33 illus.)
Disciplina 511.8
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Computer graphics
Computer science—Mathematics
Computer systems
Artificial intelligence
Design and Analysis of Algorithms
Computer Graphics
Symbolic and Algebraic Manipulation
Computer System Implementation
Artificial Intelligence
Algorismes
Complexitat computacional
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-75242-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Abundant Extensions -- Three Problems on Well-Partitioned Chordal Graphs -- Distributed Distance-r Covering Problems on Sparse High-Girth Graphs -- Reconfiguration of Connected Graph Partitions via Recombination -- Algorithms for Energy Conservation in Heterogeneous Data Centers -- On Vertex-Weighted Graph Realizations -- On the Role of 3's for the 1-2-3 Conjecture -- Upper Tail Analysis of Bucket Sort and Random Tries -- Throughput Scheduling with Equal Additive Laxity -- Fragile Complexity of Adaptive Algorithms -- FPT and Kernelization Algorithms for the Induced Tree Problem -- A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs -- Upper Dominating Set: Tight Algorithms for Pathwidth and Sub-Exponential Approximation -- A Multistage View on 2-Satisfiability -- The Weisfeiler-Leman Algorithm and Recognition of Graph Properties -- The Parameterized Suffix Tray -- Exploring the Gap Between Treedepth and Vertex Cover Through Vertex Integrity -- Covering a Set of Line Segments with a Few Squares -- Circumventing Connectivity for Kernelization -- Online and Approximate Network Construction from Bounded Connectivity Constraints -- Globally Rigid Augmentation of Minimally Rigid Graphs in \(R^2\) -- Extending Partial Representations of Rectangular Duals with Given Contact Orientations -- Can Local Optimality be Used for Efficient Data Reduction -- Colouring Graphs of Bounded Diameter in the Absence of Small Cycles -- Online Two-Dimensional Vector Packing with Advice -- Temporal Matching on Geometric Graph Data.
Record Nr. UNINA-9910483444703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
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Algorithms and Data Structures : 17th International Symposium, WADS 2021, Virtual Event, August 9–11, 2021, Proceedings / / edited by Anna Lubiw, Mohammad Salavatipour, Meng He
Algorithms and Data Structures : 17th International Symposium, WADS 2021, Virtual Event, August 9–11, 2021, Proceedings / / edited by Anna Lubiw, Mohammad Salavatipour, Meng He
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (687 pages)
Disciplina 511.8
Collana Theoretical Computer Science and General Issues
Soggetto topico Data structures (Computer science)
Information theory
Algorithms
Computer engineering
Computer networks
Computer science - Mathematics
Discrete mathematics
Computer graphics
Data Structures and Information Theory
Design and Analysis of Algorithms
Computer Engineering and Networks
Symbolic and Algebraic Manipulation
Discrete Mathematics in Computer Science
Computer Graphics
Algorismes
Estructures de dades (Informàtica)
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-83508-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adjacency Labelling of Planar Graphs (and Beyond) -- Algorithms for Explainable Clustering -- On the Spanning and Routing Ratios of the Directed Ѳ6-Graph -- The Minimum Moving Spanning Tree Problem -- Scheduling with Testing on Multiple Identical Parallel Machines -- Online Makespan Minimization With Budgeted Uncertainty -- Pattern Matching in Doubling Spaces -- Reachability Problems for Transmission Graphs -- On Minimum Generalized Manhattan Connections -- HalftimeHash: Modern Hashing without 64-bit Multipliers or Finite Fields -- Generalized Disk Graphs -- A 4-Approximation of the 2π/3 -MST -- Dynamic Dictionaries for Multisets and Counting Filters with Constant Time Operations -- The Neighborhood Polynomial of Chordal -- Incomplete Directed Perfect Phylogeny in Linear Time -- Euclidean maximum matchings in the plane—­­­­local to global -- Solving Problems on Generalized Convex Graphs via Mim-Width -- Improved Bounds on the Spanning Ratio of the Theta-5 Graph -- Computing Weighted Subset Transversals in H-Free Graphs -- Computing the Fréchet Distance Between Uncertain Curves in One Dimension -- Finding a Largest-Area Triangle in a Terrain in Near-Linear Time -- Planar Drawings with Few Slopes of Halin Graphs and Nested Pseudotrees -- An APTAS for Bin Packing with Clique-graph Conflicts -- Fast deterministic algorithms for computing all eccentricities in (hyperbolic) Helly graphs -- ANN for time series under the Fréchet distance -- Strictly In-Place Algorithms for Permuting and Inverting -- A Stronger Lower Bound on Parametric Minimum Spanning Trees -- Online bin packing of squares and cubes -- Exploration of k-Edge-Deficient Temporal Graphs -- Parameterized Complexity of Categorical Clustering with Size Constraints -- Graph Pricing With Limited Supply -- Fair Correlation Clustering with Global and Local Guarantees -- Better Distance Labeling for Unweighted Planar Graphs -- How to Catch Marathon Cheaters: New Approximation Algorithms for Tracking Paths -- Algorithms for Radius-Optimally Augmenting Trees in a Metric Space -- Upper and Lower Bounds for Fully Retroactive Graph Problem -- Characterization of Super-stable Matching -- Uniform Embeddings for Robinson Similarity Matrices -- Particle-Based Assembly Using Precise Global Control Independent Sets in Semi-random Hypergraphs -- A Query-Efficient Quantum Algorithm for Maximum Matching on General Graphs -- Support Optimality and Adaptive Cuckoo Filters -- Computing the Union Join and Subset Graph of Acyclic Hypergraphs in Subquadratic Time -- Algorithms for the Line-Constrained Disk Coverage and Related Problems -- A universal cycle for strings with fixed-content (which are also known as multiset permutations) -- Routing on Heavy-Path WSPD-Spanners -- Mapping Multiple Regions to the Grid with Bounded Hausdorff Distance -- Diverse Partitions of Colored Points -- Reverse Shortest Path Problem for Unit-Disk Graphs.
Record Nr. UNINA-9910494560003321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
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Algorithms and Discrete Applied Mathematics : 8th International Conference, CALDAM 2022, Puducherry, India, February 10–12, 2022, Proceedings / / edited by Niranjan Balachandran, R. Inkulu
Algorithms and Discrete Applied Mathematics : 8th International Conference, CALDAM 2022, Puducherry, India, February 10–12, 2022, Proceedings / / edited by Niranjan Balachandran, R. Inkulu
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (326 pages)
Disciplina 004.0151
Collana Theoretical Computer Science and General Issues
Soggetto topico Computer science—Mathematics
Algorithms
Data structures (Computer science)
Information theory
Discrete mathematics
Mathematics of Computing
Data Structures and Information Theory
Discrete Mathematics in Computer Science
Algorismes
Matemàtica discreta
Matemàtica aplicada
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-95018-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A proof of the Multiplicative 1-2-3 Conjecture -- The geodesic transversal problem on some networks -- Chromatic Bounds for Some Subclasses of $(P_3\cup P_2)$-free Graphs -- List homomorphisms to separable signed graphs -- Some position problems for graphs -- Comparability graphs among Cover-Incomparability graphs -- Complexity of Paired Domination in AT-free and Planar Graphs -- The Complexity of Star Colouring in Bounded Degree Graphs and Regular Graphs -- On Conflict-Free Spanning Tree: Algorithms and Complexity -- B0-VPG Representation of AT-free Outerplanar Graphs -- P versus NPC : Steiner tree in convex split graphs -- On cd-coloring of {P_5,K_4}-free chordal graphs -- An output-sensitive algorithm for all-pairs shortest paths in directed acyclic graphs -- Covering a Graph with Densest Subgraphs -- Coresets for $(k, \ell)$-Median Clustering under the Fréchet Distance -- Bounds and Algorithms for Geodetic Hulls -- Voronoi Games using Geodesics -- Approximation and parameterized algorithms for balanced connected partition problems -- Algorithms for Online Car-sharing Problem -- Algebraic algorithms for variants of Subset Sum -- Hardness and Approximation Results for Some Variants of Stable Marriage Problem -- On Fair Division with Binary Valuations Respecting Social Networks -- Parameterized Intractability of Defensive Alliance Problem -- On the approximability of path and cycle problems in arc-dependent networks} -- Approximation Algorithms in Graphs with Known Broadcast time of the Base Graph.
Record Nr. UNINA-9910523889203321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
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Algorithms and Discrete Applied Mathematics [[electronic resource] ] : 7th International Conference, CALDAM 2021, Rupnagar, India, February 11–13, 2021, Proceedings / / edited by Apurva Mudgal, C. R. Subramanian
Algorithms and Discrete Applied Mathematics [[electronic resource] ] : 7th International Conference, CALDAM 2021, Rupnagar, India, February 11–13, 2021, Proceedings / / edited by Apurva Mudgal, C. R. Subramanian
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (519 pages)
Disciplina 004.0151
Collana Theoretical Computer Science and General Issues
Soggetto topico Computer science—Mathematics
Algorithms
Data structures (Computer science)
Information theory
Mathematics—Data processing
Mathematics of Computing
Design and Analysis of Algorithms
Data Structures and Information Theory
Computational Mathematics and Numerical Analysis
Algorismes
Geometria
Teoria de grafs
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-67899-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Approximation Algorithms -- Online bin packing with overload cost -- Scheduling Trains with Small Stretch on a Unidirectional Line -- Algorithmic Aspects of Total Roman and Total Double Roman Domination in Graphs -- Approximation Algorithms for Orthogonal Line Centers -- Semitotal Domination on AT-free Graphs and Circle Graphs -- Burning Grids and Intervals -- Parameterized Algorithms -- On Parameterized Complexity of Liquid Democracy -- Acyclic coloring parameterized by directed clique-width. - On Structural Parameterizations of Load Coloring -- One-Sided Discrete Terrain Guarding and Chordal Graphs -- Parameterized Complexity of Locally Minimal Defensive Alliances -- Computational Geometry -- New variants of Perfect Non-crossing Matchings -- Cause I’m a Genial Imprecise Point: Outlier Detection for Uncertain Data -- A Worst-case Optimal Algorithm to Compute the Minkowski Sum of Convex Polytopes -- On the Intersections of Non-homotopic Loops -- Graph Theory -- On cd-coloring of trees and co-bipartite graphs -- Cut Vertex Transit Functions of Hypergraphs -- Lexicographic Product of Digraphs and Related Boundary-Type Sets -- The Connected Domination Number of Grids -- On degree sequences and eccentricities in pseudoline arrangement graphs. - Cops and Robber on Butterflies and Solid Grids -- b-Coloring of Some Powers of Hypercubes -- Chromatic Bounds for the Subclasses of $pK_2$ -Free Graphs -- Axiomatic characterization of the median function of a block graph -- On Coupon Coloring of Cartesian Product of Some Graphs -- On the Connectivity and the Diameter of Betweenness-Uniform Graphs. -Combinatorics and Algorithms -- On algorithms to find p-ordering -- Experimental Evaluation of a Local Search Approximation Algorithm for the Multiway Cut Problem -- Algorithmic analysis of priority-based bin packing -- Recursive methods for some problems in coding and random permutations -- Achieving positive rates with predetermined dictionaries -- Characterization of Dense Patterns Having Distinct Squares -- Graph Algorithms -- Failure and communication in a synchronized multi-drone system -- Memory Optimal Dispersion by Anonymous Mobile Robots -- Quantum and approximation algorithms for maximum witnesses of Boolean matrix products. -Template-driven Rainbow Coloring of Proper Interval Graphs -- Minimum Consistent Subset of Simple Graph Classes. - Computational Complexity -- Balanced Connected Graph Partition -- Hardness Results of Global Roman Domination in Graphs. .
Record Nr. UNISA-996464385203316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
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