Learning Theory: 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings (Lecture Notes in Computer Science, 3559, Band 3559) - Taschenbuch

EAN (ISBN-13): 9783540265566
ISBN (ISBN-10): 3540265562
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2005
Herausgeber: Springer
692 Seiten
Gewicht: 1,058 kg
Sprache: eng/Englisch

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ISBN/EAN: 3540265562

ISBN - alternative Schreibweisen:
3-540-26556-2, 978-3-540-26556-6
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: peter auer, meir, peter sem, ron, colt, ravindran
Titel des Buches: italy, conference, june, artificial intelligence, colt, proceedings

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Daten vom Verlag:

Autor/in: Peter Auer; Ron Meir
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings
Verlag: Springer; Springer Berlin
692 Seiten
Erscheinungsjahr: 2005-06-20
Berlin; Heidelberg; DE
Gewicht: 2,170 kg
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XII, 692 p.

BC; Artificial Intelligence; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Boosting; Support Vector Machine; classification; game theory; learning; learning theory; supervised learning; unsupervised learning; algorithm analysis and problem complexity; Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Mathematical Logic and Formal Languages; Artificial Intelligence; Theory of Computation; Algorithms; Formal Languages and Automata Theory; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

Learning to Rank.- Ranking and Scoring Using Empirical Risk Minimization.- Learnability of Bipartite Ranking Functions.- Stability and Generalization of Bipartite Ranking Algorithms.- Loss Bounds for Online Category Ranking.- Boosting.- Margin-Based Ranking Meets Boosting in the Middle.- Martingale Boosting.- The Value of Agreement, a New Boosting Algorithm.- Unlabeled Data, Multiclass Classification.- A PAC-Style Model for Learning from Labeled and Unlabeled Data.- Generalization Error Bounds Using Unlabeled Data.- On the Consistency of Multiclass Classification Methods.- Sensitive Error Correcting Output Codes.- Online Learning I.- Data Dependent Concentration Bounds for Sequential Prediction Algorithms.- The Weak Aggregating Algorithm and Weak Mixability.- Tracking the Best of Many Experts.- Improved Second-Order Bounds for Prediction with Expert Advice.- Online Learning II.- Competitive Collaborative Learning.- Analysis of Perceptron-Based Active Learning.- A New Perspective on an Old Perceptron Algorithm.- Support Vector Machines.- Fast Rates for Support Vector Machines.- Exponential Convergence Rates in Classification.- General Polynomial Time Decomposition Algorithms.- Kernels and Embeddings.- Approximating a Gram Matrix for Improved Kernel-Based Learning.- Learning Convex Combinations of Continuously Parameterized Basic Kernels.- On the Limitations of Embedding Methods.- Leaving the Span.- Inductive Inference.- Variations on U-Shaped Learning.- Mind Change Efficient Learning.- On a Syntactic Characterization of Classification with a Mind Change Bound.- Unsupervised Learning.- Ellipsoid Approximation Using Random Vectors.- The Spectral Method for General Mixture Models.- On Spectral Learning of Mixtures of Distributions.- From Graphs to Manifolds – Weak and Strong Pointwise Consistency of Graph Laplacians.- Towards a Theoretical Foundation for Laplacian-Based Manifold Methods.- Generalization Bounds.- Permutation Tests for Classification.- Localized Upper and Lower Bounds for Some Estimation Problems.- Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers.- Rank, Trace-Norm and Max-Norm.- Query Learning, Attribute Efficiency, Compression Schemes.- Learning a Hidden Hypergraph.- On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions.- Unlabeled Compression Schemes for Maximum Classes.- Economics and Game Theory.- Trading in Markovian Price Models.- From External to Internal Regret.- Separation Results for Learning Models.- Separating Models of Learning from Correlated and Uncorrelated Data.- Asymptotic Log-Loss of Prequential Maximum Likelihood Codes.- Teaching Classes with High Teaching Dimension Using Few Examples.- Open Problems.- Optimum Follow the Leader Algorithm.- The Cross Validation Problem.- Compute Inclusion Depth of a Pattern.