Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings: Lecture Notes in Computer Science, cartea 4754
Editat de Marcus Hutter, Rocco A. Servedio, Eiji Takimotoen Limba Engleză Paperback – 17 sep 2007
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Specificații
ISBN-13: 9783540752240
ISBN-10: 3540752242
Pagini: 422
Ilustrații: XI, 406 p.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.59 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540752242
Pagini: 422
Ilustrații: XI, 406 p.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.59 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Editors’ Introduction.- Editors’ Introduction.- Invited Papers.- A Theory of Similarity Functions for Learning and Clustering.- Machine Learning in Ecosystem Informatics.- Challenge for Info-plosion.- A Hilbert Space Embedding for Distributions.- Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity.- Invited Papers.- Feasible Iteration of Feasible Learning Functionals.- Parallelism Increases Iterative Learning Power.- Prescribed Learning of R.E. Classes.- Learning in Friedberg Numberings.- Complexity Aspects of Learning.- Separating Models of Learning with Faulty Teachers.- Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations.- Parameterized Learnability of k-Juntas and Related Problems.- On Universal Transfer Learning.- Online Learning.- Tuning Bandit Algorithms in Stochastic Environments.- Following the Perturbed Leader to Gamble at Multi-armed Bandits.- Online Regression Competitive with Changing Predictors.- Unsupervised Learning.- Cluster Identification in Nearest-Neighbor Graphs.- Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in .- Language Learning.- Learning Efficiency of Very Simple Grammars from Positive Data.- Learning Rational Stochastic Tree Languages.- Query Learning.- One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples.- Polynomial Time Algorithms for Learning k-Reversible Languages and Pattern Languages with Correction Queries.- Learning and Verifying Graphs Using Queries with a Focus on Edge Counting.- Exact Learning of Finite Unions of Graph Patterns from Queries.- Kernel-Based Learning.- Polynomial Summaries of Positive Semidefinite Kernels.- Learning Kernel Perceptrons on Noisy Data Using Random Projections.- Continuityof Performance Metrics for Thin Feature Maps.- Other Directions.- Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability.- Pseudometrics for State Aggregation in Average Reward Markov Decision Processes.- On Calibration Error of Randomized Forecasting Algorithms.