Algorithmic learning theory : 11th International Conference, ALT 2000, Sydney, Australia, December 11-13, 2000 : proceedings
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書誌事項
Algorithmic learning theory : 11th International Conference, ALT 2000, Sydney, Australia, December 11-13, 2000 : proceedings
（Lecture notes in computer science, 1968 . Lecture notes in artificial intelligence）
Springer, c2000
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Includes bibliographical references and index
内容説明・目次
内容説明
This volume contains all the papers presented at the Eleventh International C- ference on Algorithmic Learning Theory (ALT 2000) held at Coogee Holiday Inn, Sydney,Australia,11-13 December 2000. The conference was sponsored by the School of Computer Science and Engineering,University of New South Wales, and supported by the IFIP Working Group 1.4 on Computational Learning T- ory and the Computer Science Association (CSA) of Australia. In response to the call for papers 39 submissions were received on all aspects of algorithmic learning theory. Out of these 22 papers were accepted for p- sentation by the program committee. In addition,there were three invited talks by William Cohen (Whizbang Labs),Tom Dietterich (Oregon State Univeristy), and Osamu Watanabe (Tokyo Institute of Technology). This year's conference is the last in the millenium and eleventh overall in the ALT series. The ?rst ALT workshop was held in Tokyo in 1990. It was merged with the workshop on Analogical and Inductive Inference in 1994.
The conf- ence focuses on all areas related to algorithmic learning theory,including (but not limited to) the design and analysis of learning algorithms,the theory of machine learning,computational logic of/for machine discovery,inductive inf- ence,learning via queries,new learning models,scienti?c discovery,learning by analogy,arti?cial and biological neural networks,pattern recognition,statistical learning,Bayesian/MDL estimation,inductive logic programming,data m- ing and knowledge discovery,and application of learning to biological sequence analysis. In the current conference there were papers from a variety of the above areas,refelecting both the theoretical as well as practical aspects of learning.
目次
INVITED LECTURES.- Extracting Information from the Web for Concept Learning and Collaborative Filtering.- The Divide-and-Conquer Manifesto.- Sequential Sampling Techniques for Algorithmic Learning Theory.- REGULAR CONTRIBUTIONS.- Towards an Algorithmic Statistics.- Minimum Message Length Grouping of Ordered Data.- Learning From Positive and Unlabeled Examples.- Learning Erasing Pattern Languages with Queries.- Learning Recursive Concepts with Anomalies.- Identification of Function Distinguishable Languages.- A Probabilistic Identification Result.- A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System.- Hypotheses Finding via Residue Hypotheses with the Resolution Principle.- Conceptual Classifications Guided by a Concept Hierarchy.- Learning Taxonomic Relation by Case-based Reasoning.- Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees.- Self-duality of Bounded Monotone Boolean Functions and Related Problems.- Sharper Bounds for the Hardness of Prototype and Feature Selection.- On the Hardness of Learning Acyclic Conjunctive Queries.- Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm.- On Approximate Learning by Multi-layered Feedforward Circuits.- The Last-Step Minimax Algorithm.- Rough Sets and Ordinal Classification.- A note on the generalization performance of kernel classifiers with margin.- On the Noise Model of Support Vector Machines Regression.- Computationally Efficient Transductive Machines.
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