Algorithmic Learning Theory 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings /

This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1-4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Hutter, Marcus (Editor ), Servedio, Rocco A. (Editor ), Takimoto, Eiji (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:Lecture Notes in Artificial Intelligence, 4754
Temas:
Artificial intelligence.
Data mining.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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
  • Continuity of 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.

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