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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->
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others]
| Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2021 |
| Descrizione fisica | xxxvi, 405 pages |
| Soggetto topico |
earth sciences
climatology data science remote sensing machine learning Algorithms - Study and teaching |
| ISBN |
1-119-64616-2
1-119-64618-9 1-119-64615-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555005503321 |
| Hoboken, New Jersey : , : Wiley, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others]
| Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2021 |
| Descrizione fisica | xxxvi, 405 pages |
| Disciplina | 550.71 |
| Soggetto topico |
earth sciences
climatology data science remote sensing machine learning Algorithms - Study and teaching |
| ISBN |
1-119-64616-2
1-119-64618-9 1-119-64615-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910677724503321 |
| Hoboken, New Jersey : , : Wiley, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Energy Dissipation and Vibration Control : Modeling, Algorithm and Devices / / Gangbing Song, Steve C. S. Cai, Hong-Nan Li
| Energy Dissipation and Vibration Control : Modeling, Algorithm and Devices / / Gangbing Song, Steve C. S. Cai, Hong-Nan Li |
| Autore | Song Gangbing |
| Pubbl/distr/stampa | Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2018 |
| Descrizione fisica | 1 online resource (262 pages) |
| Disciplina | 518.1 |
| Soggetto topico | Algorithms - Study and teaching |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Energy Dissipation and Vibration Control |
| Record Nr. | UNINA-9910674024203321 |
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Song Gangbing
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| Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Explaining algorithms using metaphors / / Michal Forisek, Monika Steinova
| Explaining algorithms using metaphors / / Michal Forisek, Monika Steinova |
| Autore | Forisek Michal |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | New York, : Springer, 2013 |
| Descrizione fisica | 1 online resource (x, 94 pages) : illustrations |
| Disciplina | 374.26 |
| Altri autori (Persone) | SteinovaMonika |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Algorithms - Study and teaching
Algorithms |
| ISBN | 1-4471-5019-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Graph Algorithms -- Computational Geometry -- Strings and Sequences -- Solutions to Exercises. |
| Record Nr. | UNINA-9910741193303321 |
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Forisek Michal
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| New York, : Springer, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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How to think about algorithms / / Jeff Edmonds [[electronic resource]]
| How to think about algorithms / / Jeff Edmonds [[electronic resource]] |
| Autore | Edmonds Jeff <1963-> |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2008 |
| Descrizione fisica | 1 online resource (xiii, 448 pages) : digital, PDF file(s) |
| Disciplina | 518/.1 |
| Soggetto topico |
Algorithms - Study and teaching
Loops (Group theory) - Study and teaching Invariants - Study and teaching Recursion theory - Study and teaching |
| ISBN |
1-107-17584-4
0-511-64579-1 9786612390289 1-282-39028-7 1-139-63726-6 0-511-80824-0 0-511-64988-6 0-511-41278-9 0-511-56800-2 0-511-41370-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Iterative algorithms: measures of progress and loop invariants -- Examples using more-of-the-input loop invariants -- Abstract data types -- Narrowing the search space: binary search -- Iterative sorting algorithms -- Euclid's GCD algorithm -- The loop invariant for lower bounds -- Abstractions, techniques, and theory -- Some simple examples of recursive algorithms -- Recursion on trees -- Recursive images -- Parsing with context-free grammars -- Definition of optimization problems -- Graph search algorithms -- Network flows and linear programming -- Greedy algorithms -- Recursive backtracking -- Dynamic programming algorithms -- Examples of dynamic programs -- Reductions and NP-completeness -- Randomized algorithms -- Existential and universal quantifiers -- Time complexity -- Logarithms and exponentials -- Asymptotic growth -- Adding-made-easy approximations -- Recurrence relations -- A formal proof of correctness. |
| Record Nr. | UNINA-9910454516203321 |
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Edmonds Jeff <1963->
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| Cambridge : , : Cambridge University Press, , 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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How to think about algorithms / / Jeff Edmonds [[electronic resource]]
| How to think about algorithms / / Jeff Edmonds [[electronic resource]] |
| Autore | Edmonds Jeff <1963-> |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2008 |
| Descrizione fisica | 1 online resource (xiii, 448 pages) : digital, PDF file(s) |
| Disciplina | 518/.1 |
| Soggetto topico |
Algorithms - Study and teaching
Loops (Group theory) - Study and teaching Invariants - Study and teaching Recursion theory - Study and teaching |
| ISBN |
1-107-17584-4
0-511-64579-1 9786612390289 1-282-39028-7 1-139-63726-6 0-511-80824-0 0-511-64988-6 0-511-41278-9 0-511-56800-2 0-511-41370-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Iterative algorithms: measures of progress and loop invariants -- Examples using more-of-the-input loop invariants -- Abstract data types -- Narrowing the search space: binary search -- Iterative sorting algorithms -- Euclid's GCD algorithm -- The loop invariant for lower bounds -- Abstractions, techniques, and theory -- Some simple examples of recursive algorithms -- Recursion on trees -- Recursive images -- Parsing with context-free grammars -- Definition of optimization problems -- Graph search algorithms -- Network flows and linear programming -- Greedy algorithms -- Recursive backtracking -- Dynamic programming algorithms -- Examples of dynamic programs -- Reductions and NP-completeness -- Randomized algorithms -- Existential and universal quantifiers -- Time complexity -- Logarithms and exponentials -- Asymptotic growth -- Adding-made-easy approximations -- Recurrence relations -- A formal proof of correctness. |
| Record Nr. | UNINA-9910782417503321 |
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Edmonds Jeff <1963->
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| Cambridge : , : Cambridge University Press, , 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent data engineering and automated learning : IDEAL 2008 : 9th international conference, Daejeon, South Korea, November 2-5, 2008 : proceedings / / Colin Fyfe [and three others], editors
| Intelligent data engineering and automated learning : IDEAL 2008 : 9th international conference, Daejeon, South Korea, November 2-5, 2008 : proceedings / / Colin Fyfe [and three others], editors |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer Verlag, , [2008] |
| Descrizione fisica | 1 online resource (XVI, 534 p.) |
| Disciplina | 004.071 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Computer science - Research
Algorithms - Study and teaching |
| ISBN | 3-540-88906-X |
| Classificazione | 54.64 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Learning and Information Processing -- Proposal of Exploitation-Oriented Learning PS-r# -- Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting -- Hybrid Weighted Distance Measures and Their Application to Pattern Recognition -- A Multitask Learning Approach to Face Recognition Based on Neural Networks -- Logic Synthesis for FSMs Using Quantum Inspired Evolution -- A New Adaptive Strategy for Pruning and Adding Hidden Neurons during Training Artificial Neural Networks -- Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal Classification -- A Semi-fragile Watermark Scheme Based on the Logistic Chaos Sequence and Singular Value Decomposition -- Distribution Feeder Load Balancing Using Support Vector Machines -- Extracting Auto-Correlation Feature for License Plate Detection Based on AdaBoost -- Evolutionary Optimization of Union-Based Rule-Antecedent Fuzzy Neural Networks and Its Applications -- Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention -- Top-Down Object Color Biased Attention Using Growing Fuzzy Topology ART -- A Study on Human Gaze Estimation Using Screen Reflection -- A Novel GA-Taguchi-Based Feature Selection Method -- Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation -- Automatic Order of Data Points in RE Using Neural Networks -- Orthogonal Nonnegative Matrix Factorization: Multiplicative Updates on Stiefel Manifolds -- Feature Discovery by Enhancement and Relaxation of Competitive Units -- Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification -- A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks -- Semi-supervised Learning with Ensemble Learning and Graph Sharpening -- Exploring Topology Preservation of SOMs with a Graph Based Visualization -- A Class of Novel Kernel Functions -- Data Mining and Information Management -- RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database -- Extracting Key Entities and Significant Events from Online Daily News -- Performance Evaluation of Intelligent Prediction Models on Smokers’ Quitting Behaviour -- Range Facial Recognition with the Aid of Eigenface and Morphological Neural Networks -- Modular Bayesian Network Learning for Mobile Life Understanding -- Skin Pores Detection for Image-Based Skin Analysis -- An Empirical Research on Extracting Relations from Wikipedia Text -- A Data Perturbation Method by Field Rotation and Binning by Averages Strategy for Privacy Preservation -- Mining Weighted Frequent Patterns Using Adaptive Weights -- On the Improvement of the Mapping Trustworthiness and Continuity of a Manifold Learning Model -- Guaranteed Network Traffic Demand Prediction Using FARIMA Models -- A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets -- The Use of Semi-parametric Methods for Feature Extraction in Mobile Cellular Networks -- Personalized Document Summarization Using Non-negative Semantic Feature and Non-negative Semantic Variable -- Bioinformatics and Neuroinformatics -- Cooperative E-Organizations for Distributed Bioinformatics Experiments -- Personal Knowledge Network Reconfiguration Based on Brain Like Function Using Self Type Matching Strategy -- A Theoretical Derivation of the Kernel Extreme Energy Ratio Method for EEG Feature Extraction -- Control of a Wheelchair by Motor Imagery in Real Time -- Robust Vessel Segmentation Based on Multi-resolution Fuzzy Clustering -- Building a Spanish MMTx by Using Automatic Translation and Biomedical Ontologies -- Compensation for Speed-of-Processing Effects in EEG-Data Analysis -- Statistical Baselines from Random Matrix Theory -- Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain Computer Interfacing -- Agents and Distributed Systems -- Improving the Relational Evaluation of XML Queries by Means of Path Summaries -- Identification of the Inverse Dynamics Model: A Multiple Relevance Vector Machines Approach -- When Is Inconsistency Considered Harmful: Temporal Characterization of Knowledge Base Inconsistency -- Intelligent Engineering and Its Application in Policy Simulation -- Design of Directory Facilitator for Agent-Based Service Discovery in Ubiquitous Computing Environments -- Financial Engineering and Modeling -- Laboratory of Policy Study on Electricity Demand Forecasting by Intelligent Engineering -- Self-adaptive Mutation Only Genetic Algorithm: An Application on the Optimization of Airport Capacity Utilization -- Cross Checking Rules to Improve Consistency between UML Static Diagram and Dynamic Diagram -- Neural Networks Approach to the Detection of Weekly Seasonality in Stock Trading -- Invited Session -- Bregman Divergences and the Self Organising Map -- Feature Locations in Images -- A Hierarchical Self-organised Classification of ‘Multinational’ Corporations -- An Adaptive Image Watermarking Scheme Using Non-separable Wavelets and Support Vector Regression -- Cluster Analysis of Land-Cover Images Using Automatically Segmented SOMs with Textural Information -- Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry -- AI for Modelling the Laser Milling of Copper Components -- Country and Political Risk Analysis of Spanish Multinational Enterprises Using Exploratory Projection Pursuit -- Single-Layer Neural Net Competes with Multi-layer Neural Net -- Semi-supervised Growing Neural Gas for Face Recognition. |
| Record Nr. | UNISA-996466352503316 |
| Berlin ; ; Heidelberg : , : Springer Verlag, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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