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Algorithmic Learning Theory [electronic resource] :11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings /

Contributor(s): Arimura, Hiroki [editor.] | Jain, Sanjay [editor.] | Sharma, Arun [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1968Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.Description: XII, 348 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540409922.Subject(s): Computer science | Computer programming | Computers | Algorithms | Mathematical logic | Artificial intelligence | Text processing (Computer science) | Computer Science | Artificial Intelligence (incl. Robotics) | Programming Techniques | Computation by Abstract Devices | Algorithm Analysis and Problem Complexity | Mathematical Logic and Formal Languages | Document Preparation and Text ProcessingOnline resources: Click here to access online
Contents:
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.
In: Springer eBooks
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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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