Algorithmic Learning Theory  16th International Conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings /  [electronic resource] : 
edited by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita. 
 - 1st ed. 2005. 
 - XII, 491 p.  online resource. 
 - Lecture Notes in Artificial Intelligence,  3734  2945-9141 ; .
 - Lecture Notes in Artificial Intelligence,  3734 .
<br/><br/>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. 
<br/><br/><label>ISBN: </label>9783540316961 
<br/><br/><label>Standard No.: </label>10.1007/11564089  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence.<br/>Computer science.<br/>Algorithms.<br/>Machine theory.<br/>Natural language processing (Computer science).<br/>Artificial Intelligence.<br/>Theory of Computation.<br/>Algorithms.<br/>Formal Languages and Automata Theory.<br/>Natural Language Processing (NLP).
<br/><br/><label>LC Class. No.: </label>Q334-342 TA347.A78 
<br/><br/><label>Dewey Class. No.: </label>006.3