Algorithmic Learning Theory : 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings / / edited by Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003
• Edizione: 1st ed. 2003.
• Descrizione fisica: 1 online resource (XII, 320 p.)
• Disciplina: 006.3/1
• Soggetto topico: Artificial intelligence; Computers; Algorithms; Logic, Symbolic and mathematical; 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)
• Persona (resp. second.): GavaldàRicard; JantkeKlaus P; TakimotoEiji
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Invited Papers -- Abduction and the Dualization Problem -- Signal Extraction and Knowledge Discovery Based on Statistical Modeling -- Association Computation for Information Access -- Efficient Data Representations That Preserve Information -- Can Learning in the Limit Be Done Efficiently? -- Inductive Inference -- Intrinsic Complexity of Uniform Learning -- On Ordinal VC-Dimension and Some Notions of Complexity -- Learning of Erasing Primitive Formal Systems from Positive Examples -- Changing the Inference Type – Keeping the Hypothesis Space -- Learning and Information Extraction -- Robust Inference of Relevant Attributes -- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables -- Learning with Queries -- On the Learnability of Erasing Pattern Languages in the Query Model -- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries -- Learning with Non-linear Optimization -- Kernel Trick Embedded Gaussian Mixture Model -- Efficiently Learning the Metric with Side-Information -- Learning Continuous Latent Variable Models with Bregman Divergences -- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation -- Learning from Random Examples -- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays -- Learning a Subclass of Regular Patterns in Polynomial Time -- Identification with Probability One of Stochastic Deterministic Linear Languages -- Online Prediction -- Criterion of Calibration for Transductive Confidence Machine with Limited Feedback -- Well-Calibrated Predictions from Online Compression Models -- Transductive Confidence Machine Is Universal -- On the Existence and Convergence of Computable Universal Priors.
• Titolo autorizzato: Algorithmic Learning Theory
• ISBN: 3-540-39624-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910144028803321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence ; ; 2842