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Algorithmic learning theory : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, proceedings / / Yoav Freund [and three others], (Eds.)
| Algorithmic learning theory : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, proceedings / / Yoav Freund [and three others], (Eds.) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (XIII, 467 p.) |
| Disciplina | 005.1 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer algorithms
Machine learning |
| ISBN | 3-540-87987-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- On Iterative Algorithms with an Information Geometry Background -- Visual Analytics: Combining Automated Discovery with Interactive Visualizations -- Some Mathematics behind Graph Property Testing -- Finding Total and Partial Orders from Data for Seriation -- Computational Models of Neural Representations in the Human Brain -- Regular Contributions -- Generalization Bounds for Some Ordinal Regression Algorithms -- Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm -- Sample Selection Bias Correction Theory -- Exploiting Cluster-Structure to Predict the Labeling of a Graph -- A Uniform Lower Error Bound for Half-Space Learning -- Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces -- Learning and Generalization with the Information Bottleneck -- Growth Optimal Investment with Transaction Costs -- Online Regret Bounds for Markov Decision Processes with Deterministic Transitions -- On-Line Probability, Complexity and Randomness -- Prequential Randomness -- Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor -- Nonparametric Independence Tests: Space Partitioning and Kernel Approaches -- Supermartingales in Prediction with Expert Advice -- Aggregating Algorithm for a Space of Analytic Functions -- Smooth Boosting for Margin-Based Ranking -- Learning with Continuous Experts Using Drifting Games -- Entropy Regularized LPBoost -- Optimally Learning Social Networks with Activations and Suppressions -- Active Learning in Multi-armed Bandits -- Query Learning and Certificates in Lattices -- Clustering with Interactive Feedback -- Active Learning of Group-Structured Environments -- Finding the Rare Cube -- Iterative Learning of Simple External Contextual Languages -- Topological Properties of Concept Spaces -- Dynamically Delayed Postdictive Completeness and Consistency in Learning -- Dynamic Modeling in Inductive Inference -- Optimal Language Learning -- Numberings Optimal for Learning -- Learning with Temporary Memory -- Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors. |
| Record Nr. | UNINA-9910484043803321 |
| Berlin ; ; Heidelberg : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithmic learning theory : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, proceedings / / Yoav Freund [and three others], (Eds.)
| Algorithmic learning theory : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, proceedings / / Yoav Freund [and three others], (Eds.) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (XIII, 467 p.) |
| Disciplina | 005.1 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer algorithms
Machine learning |
| ISBN | 3-540-87987-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- On Iterative Algorithms with an Information Geometry Background -- Visual Analytics: Combining Automated Discovery with Interactive Visualizations -- Some Mathematics behind Graph Property Testing -- Finding Total and Partial Orders from Data for Seriation -- Computational Models of Neural Representations in the Human Brain -- Regular Contributions -- Generalization Bounds for Some Ordinal Regression Algorithms -- Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm -- Sample Selection Bias Correction Theory -- Exploiting Cluster-Structure to Predict the Labeling of a Graph -- A Uniform Lower Error Bound for Half-Space Learning -- Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces -- Learning and Generalization with the Information Bottleneck -- Growth Optimal Investment with Transaction Costs -- Online Regret Bounds for Markov Decision Processes with Deterministic Transitions -- On-Line Probability, Complexity and Randomness -- Prequential Randomness -- Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor -- Nonparametric Independence Tests: Space Partitioning and Kernel Approaches -- Supermartingales in Prediction with Expert Advice -- Aggregating Algorithm for a Space of Analytic Functions -- Smooth Boosting for Margin-Based Ranking -- Learning with Continuous Experts Using Drifting Games -- Entropy Regularized LPBoost -- Optimally Learning Social Networks with Activations and Suppressions -- Active Learning in Multi-armed Bandits -- Query Learning and Certificates in Lattices -- Clustering with Interactive Feedback -- Active Learning of Group-Structured Environments -- Finding the Rare Cube -- Iterative Learning of Simple External Contextual Languages -- Topological Properties of Concept Spaces -- Dynamically Delayed Postdictive Completeness and Consistency in Learning -- Dynamic Modeling in Inductive Inference -- Optimal Language Learning -- Numberings Optimal for Learning -- Learning with Temporary Memory -- Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors. |
| Record Nr. | UNISA-996465373803316 |
| Berlin ; ; Heidelberg : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Boosting : foundations and algorithms / / Robert E. Schapire and Yoav Freund
| Boosting : foundations and algorithms / / Robert E. Schapire and Yoav Freund |
| Autore | Schapire Robert E. |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : MIT Press, , c2012 |
| Descrizione fisica | 1 online resource (544 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | FreundYoav |
| Collana | Adaptive computation and machine learning series |
| Soggetto topico |
Boosting (Algorithms)
Supervised learning (Machine learning) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-67835-6
9786613655288 0-262-30118-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time. |
| Record Nr. | UNINA-9910260629203321 |
Schapire Robert E.
|
||
| Cambridge, Massachusetts : , : MIT Press, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Boosting : foundations and algorithms / / Robert E. Schapire and Yoav Freund
| Boosting : foundations and algorithms / / Robert E. Schapire and Yoav Freund |
| Autore | Schapire Robert E. |
| Pubbl/distr/stampa | Cambridge, : The MIT Press, 2012 |
| Descrizione fisica | 1 online resource (544 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | FreundYoav |
| Collana | Adaptive computation and machine learning series |
| Soggetto topico |
Boosting (Algorithms)
Supervised learning (Machine learning) |
| Soggetto non controllato |
Artificial intelligence
Algorithms and data structures |
| ISBN |
0-262-30039-7
1-280-67835-6 9786613655288 0-262-30118-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time. |
| Record Nr. | UNINA-9910529509803321 |
|
Schapire Robert E.
|
||
| Cambridge, : The MIT Press, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||