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Algorithmic Learning Theory [electronic resource] :14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings /

Contributor(s): Gavaldá, Ricard [editor.] | Jantke, Klaus P [editor.] | Takimoto, Eiji [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 2842Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2003.Description: XII, 320 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540396246.Subject(s): Computer science | Computers | Algorithms | Mathematical logic | Artificial intelligence | Text processing (Computer science) | Computer Science | Artificial Intelligence (incl. Robotics) | 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 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.
In: Springer eBooks
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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.

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