| Description |
1 online resource (xi, 402 pages) : illustrations |
| Series |
Lecture notes in computer science, 0302-9743 ; 4754. Lecture notes in artificial intelligence |
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Lecture notes in computer science ; 4754.
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Lecture notes in computer science. Lecture notes in artificial intelligence
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| Contents |
Front Matter; Editors' Introduction; Machine Learning in Ecosystem Informatics; A Hilbert Space Embedding for Distributions; Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity; Feasible Iteration of Feasible Learning Functionals; Parallelism Increases Iterative Learning Power; Prescribed Learning of R.E. Classes; Learning in Friedberg Numberings; Separating Models of Learning with Faulty Teachers; Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations; Parameterized Learnability of k -Juntas and Related Problems |
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On Universal Transfer LearningTuning Bandit Algorithms in Stochastic Environments; Following the Perturbed Leader to Gamble at Multi-armed Bandits; Online Regression Competitive with Changing Predictors; Cluster Identification in Nearest-Neighbor Graphs; Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in \mathbb Rd; Learning Efficiency of Very Simple Grammars from Positive Data; Learning Rational Stochastic Tree Languages; One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples |
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Polynomial Time Algorithms for Learning k -Reversible Languages and Pattern Languages with Correction QueriesLearning and Verifying Graphs Using Queries with a Focus on Edge Counting; Exact Learning of Finite Unions of Graph Patterns from Queries; Polynomial Summaries of Positive Semidefinite Kernels; Learning Kernel Perceptrons on Noisy Data Using Random Projections; Continuity of Performance Metrics for Thin Feature Maps; Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability; Pseudometrics for State Aggregation in Average Reward Markov Decision Processes |
| Summary |
This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, colocated with the 10th International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of 5 invited papers were carefully reviewed and selected from 50 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector m |
| Analysis |
computerwetenschappen |
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computer sciences |
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kunstmatige intelligentie |
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artificial intelligence |
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datamining |
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data mining |
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Information and Communication Technology (General) |
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Informatie- en communicatietechnologie (algemeen) |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record |
| Subject |
Computer algorithms -- Congresses
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Machine learning -- Congresses
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Computer algorithms.
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Machine learning.
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Informatique.
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Computer algorithms
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Machine learning
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Hutter, Marcus.
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Servedio, Rocco A.
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Takimoto, Eiji, 1964-
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| ISBN |
9783540752257 |
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3540752250 |
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9788354075226 |
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8354075222 |
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