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Algorithmic Learning Theory [[electronic resource] ] : 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings / / edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles
| Algorithmic Learning Theory [[electronic resource] ] : 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings / / edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIX, 371 p. 21 illus.) |
| Disciplina | 005.1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computers Data mining Pattern recognition Artificial Intelligence Theory of Computation Data Mining and Knowledge Discovery Pattern Recognition |
| ISBN | 3-319-46379-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Error bounds, sample compression schemes -- Statistical learning, theory, evolvability -- Exact and interactive learning -- Complexity of teaching models -- Inductive inference -- Online learning -- Bandits and reinforcement learning -- Clustering. |
| Record Nr. | UNISA-996465274103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Algorithmic Learning Theory : 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings / / edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles
| Algorithmic Learning Theory : 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings / / edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIX, 371 p. 21 illus.) |
| Disciplina | 005.1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computers Data mining Pattern recognition Artificial Intelligence Theory of Computation Data Mining and Knowledge Discovery Pattern Recognition |
| ISBN | 3-319-46379-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Error bounds, sample compression schemes -- Statistical learning, theory, evolvability -- Exact and interactive learning -- Complexity of teaching models -- Inductive inference -- Online learning -- Bandits and reinforcement learning -- Clustering. |
| Record Nr. | UNINA-9910483307903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithmic Learning Theory [[electronic resource] ] : 16th International Conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings / / edited by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita
| Algorithmic Learning Theory [[electronic resource] ] : 16th International Conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings / / edited by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 |
| Descrizione fisica | 1 online resource (XII, 491 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computers Algorithms Mathematical logic Natural language processing (Computer science) Artificial Intelligence Computation by Abstract Devices Algorithm Analysis and Problem Complexity Mathematical Logic and Formal Languages Natural Language Processing (NLP) |
| ISBN |
3-540-31696-5
3-540-29242-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Editors’ Introduction -- Editors’ Introduction -- Invited Papers -- Invention and Artificial Intelligence -- The Arrowsmith Project: 2005 Status Report -- The Robot Scientist Project -- Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources -- Training Support Vector Machines via SMO-Type Decomposition Methods -- Kernel-Based Learning -- Measuring Statistical Dependence with Hilbert-Schmidt Norms -- An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron -- Learning Causal Structures Based on Markov Equivalence Class -- Stochastic Complexity for Mixture of Exponential Families in Variational Bayes -- ACME: An Associative Classifier Based on Maximum Entropy Principle -- Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors -- On Computability of Pattern Recognition Problems -- PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance -- Learnability of Probabilistic Automata via Oracles -- Learning Attribute-Efficiently with Corrupt Oracles -- Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution -- Learning of Elementary Formal Systems with Two Clauses Using Queries -- Gold-Style and Query Learning Under Various Constraints on the Target Class -- Non U-Shaped Vacillatory and Team Learning -- Learning Multiple Languages in Groups -- Inferring Unions of the Pattern Languages by the Most Fitting Covers -- Identification in the Limit of Substitutable Context-Free Languages -- Algorithms for Learning Regular Expressions -- A Class of Prolog Programs with Non-linear Outputs Inferable from Positive Data -- Absolute Versus Probabilistic Classification in a Logical Setting -- Online Allocation with Risk Information -- Defensive Universal Learning with Experts -- On Following the Perturbed Leader in the Bandit Setting -- Mixture of Vector Experts -- On-line Learning with Delayed Label Feedback -- Monotone Conditional Complexity Bounds on Future Prediction Errors -- Non-asymptotic Calibration and Resolution -- Defensive Prediction with Expert Advice -- Defensive Forecasting for Linear Protocols -- Teaching Learners with Restricted Mind Changes. |
| Record Nr. | UNISA-996466223003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Learning Theory [[electronic resource] ] : 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings / / edited by Hans Ulrich Simon, Gábor Lugosi
| Learning Theory [[electronic resource] ] : 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings / / edited by Hans Ulrich Simon, Gábor Lugosi |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XII, 660 p.) |
| Disciplina | 006.3/1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computers Algorithms Mathematical logic Artificial Intelligence Computation by Abstract Devices Algorithm Analysis and Problem Complexity Mathematical Logic and Formal Languages |
| ISBN | 3-540-35296-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Presentations -- Random Multivariate Search Trees -- On Learning and Logic -- Predictions as Statements and Decisions -- Clustering, Un-, and Semisupervised Learning -- A Sober Look at Clustering Stability -- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption -- Stable Transductive Learning -- Uniform Convergence of Adaptive Graph-Based Regularization -- Statistical Learning Theory -- The Rademacher Complexity of Linear Transformation Classes -- Function Classes That Approximate the Bayes Risk -- Functional Classification with Margin Conditions -- Significance and Recovery of Block Structures in Binary Matrices with Noise -- Regularized Learning and Kernel Methods -- Maximum Entropy Distribution Estimation with Generalized Regularization -- Unifying Divergence Minimization and Statistical Inference Via Convex Duality -- Mercer’s Theorem, Feature Maps, and Smoothing -- Learning Bounds for Support Vector Machines with Learned Kernels -- Query Learning and Teaching -- On Optimal Learning Algorithms for Multiplicity Automata -- Exact Learning Composed Classes with a Small Number of Mistakes -- DNF Are Teachable in the Average Case -- Teaching Randomized Learners -- Inductive Inference -- Memory-Limited U-Shaped Learning -- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples -- Learning Rational Stochastic Languages -- Parent Assignment Is Hard for the MDL, AIC, and NML Costs -- Learning Algorithms and Limitations on Learning -- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention -- Discriminative Learning Can Succeed Where Generative Learning Fails -- Improved Lower Bounds for Learning Intersections of Halfspaces -- Efficient Learning Algorithms Yield Circuit Lower Bounds -- Online Aggregation -- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition -- Aggregation and Sparsity Via ?1 Penalized Least Squares -- A Randomized Online Learning Algorithm for Better Variance Control -- Online Prediction and Reinforcement Learning I -- Online Learning with Variable Stage Duration -- Online Learning Meets Optimization in the Dual -- Online Tracking of Linear Subspaces -- Online Multitask Learning -- Online Prediction and Reinforcement Learning II -- The Shortest Path Problem Under Partial Monitoring -- Tracking the Best Hyperplane with a Simple Budget Perceptron -- Logarithmic Regret Algorithms for Online Convex Optimization -- Online Variance Minimization -- Online Prediction and Reinforcement Learning III -- Online Learning with Constraints -- Continuous Experts and the Binning Algorithm -- Competing with Wild Prediction Rules -- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path -- Other Approaches -- Ranking with a P-Norm Push -- Subset Ranking Using Regression -- Active Sampling for Multiple Output Identification -- Improving Random Projections Using Marginal Information -- Open Problems -- Efficient Algorithms for General Active Learning -- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. |
| Record Nr. | UNISA-996466132703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||