Pakistan journal of plant sciences

• Record Nr.: UNISA996336387703316
• Pubbl/distr/stampa: Peshawar, Pakistan, : Dept. of Botany, University of Peshawar, [1995]-
• Descrizione fisica: 1 online resource
• Soggetti: Botany; Botany - Pakistan; Periodicals.; Pakistan
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Periodico
• Note generali: Refereed/Peer-reviewed; Title from cover.

Algorithmic Learning Theory : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings / / edited by Yoav Freund, László Györfi, György Turán, Thomas Zeugmann

• Record Nr.: UNINA9910484043803321
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2008
• ISBN: 3-540-87987-0
• Edizione: [1st ed. 2008.]
• Descrizione fisica: 1 online resource (XIII, 467 p.)
• Collana: Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 5254
• Disciplina: 005.1
• Soggetti: Data mining; Artificial intelligence; Natural language processing (Computer science); Digital humanities; Data Mining and Knowledge Discovery; Artificial Intelligence; Natural Language Processing (NLP); Digital Humanities
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• 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.
• Sommario/riassunto: This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.