18. ALT 2007: Sendai, Japan

Marcus Hutter, Rocco A. Servedio, Eiji Takimoto (Eds.): Algorithmic Learning Theory, 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings. Lecture Notes in Computer Science 4754 Springer 2007, ISBN 978-3-540-75224-0 BibTeX

Invited Papers

Avrim Blum:
A Theory of Similarity Functions for Learning and Clustering. 9
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Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics. 10-11
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Masaru Kitsuregawa:
Challenge for Info-plosion. 12
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Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. 13-31
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Jürgen Schmidhuber:
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity. 32-33
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John Case, Timo Kötzing, Todd Paddock:
Feasible Iteration of Feasible Learning Functionals. 34-48
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John Case, Samuel E. Moelius:
Parallelism Increases Iterative Learning Power. 49-63
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Sanjay Jain, Frank Stephan, Ye Nan:
Prescribed Learning of R.E. Classes. 64-78
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Sanjay Jain, Frank Stephan:
Learning in Friedberg Numberings. 79-93
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Vitaly Feldman, Shrenik Shah, Neal Wadhwa:
Separating Models of Learning with Faulty Teachers. 94-106
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César Luis Alonso, José Luis Montaña:
Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations. 107-119
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Vikraman Arvind, Johannes Köbler, Wolfgang Lindner:
Parameterized Learnability of k -Juntas and Related Problems. 120-134
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M. M. Hassan Mahmud:
On Universal Transfer Learning. 135-149
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Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári:
Tuning Bandit Algorithms in Stochastic Environments. 150-165
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Jussi Kujala, Tapio Elomaa:
Following the Perturbed Leader to Gamble at Multi-armed Bandits. 166-180
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Steven Busuttil, Yuri Kalnishkan:
Online Regression Competitive with Changing Predictors. 181-195
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Markus Maier, Matthias Hein, Ulrike von Luxburg:
Cluster Identification in Nearest-Neighbor Graphs. 196-210
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Kevin L. Chang:
Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in \mathbb R^d. 211-226
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Ryo Yoshinaka:
Learning Efficiency of Very Simple Grammars from Positive Data. 227-241
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François Denis, Amaury Habrard:
Learning Rational Stochastic Tree Languages. 242-256
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Sanjay Jain, Efim B. Kinber:
One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples. 257-271
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Cristina Tîrnauca, Timo Knuutila:
Polynomial Time Algorithms for Learning k -Reversible Languages and Pattern Languages with Correction Queries. 272-284
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Lev Reyzin, Nikhil Srivastava:
Learning and Verifying Graphs Using Queries with a Focus on Edge Counting. 285-297
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Rika Okada, Satoshi Matsumoto, Tomoyuki Uchida, Yusuke Suzuki, Takayoshi Shoudai:
Exact Learning of Finite Unions of Graph Patterns from Queries. 298-312
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Kilho Shin, Tetsuji Kuboyama:
Polynomial Summaries of Positive Semidefinite Kernels. 313-327
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Guillaume Stempfel, Liva Ralaivola:
Learning Kernel Perceptrons on Noisy Data Using Random Projections. 328-342
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Adam Kowalczyk:
Continuity of Performance Metrics for Thin Feature Maps. 343-357
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Takafumi Kanamori:
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. 358-372
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Ronald Ortner:
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes. 373-387
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Vladimir V. V'yugin:
On Calibration Error of Randomized Forecasting Algorithms. 388-402
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