Description 
1 online resource (xi, 402 pages) : illustrations 
Series 
Lecture notes in computer science, 03029743 ; 4754. Lecture notes in artificial intelligence 

Lecture notes in computer science ; 4754


Lecture notes in computer science. Lecture notes in artificial intelligence.

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; VapnikChervonenkis Dimension of Parallel Arithmetic Computations; Parameterized Learnability of k Juntas and Related Problems 

On Universal Transfer LearningTuning Bandit Algorithms in Stochastic Environments; Following the Perturbed Leader to Gamble at Multiarmed Bandits; Online Regression Competitive with Changing Predictors; Cluster Identification in NearestNeighbor 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; OneShot Learners Using Negative Counterexamples and Nearest Positive Examples 

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 14, 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, online learning, inductive inference, algorithmic forecasting, boosting, support vector m 
Bibliography 
Includes bibliographical references and index 
Notes 
Print version record 
Subject 
Computer algorithms  Congresses.


Machine learning  Congresses.

Genre/Form 
Conference papers and proceedings.


Conference papers and proceedings.

Form 
Electronic book

Author 
Hutter, Marcus.


Servedio, Rocco A.


Takimoto, Eiji, 1964

ISBN 
9783540752257 

3540752250 
