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ORCID18. ALT 2007: Sendai, Japan
- Marcus Hutter
, Rocco A. Servedio, Eiji Takimoto:
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 - Marcus Hutter, Rocco A. Servedio, Eiji Takimoto:
Editors' Introduction. 1-8
Invited Papers
- Avrim Blum:
A Theory of Similarity Functions for Learning and Clustering. 9 - Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics. 10-11 - Masaru Kitsuregawa:
Challenge for Info-plosion. 12 - Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. 13-31 - Jürgen Schmidhuber:
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity. 32-33
Inductive Inference
- John Case, Timo Kötzing, Todd Paddock:
Feasible Iteration of Feasible Learning Functionals. 34-48 - John Case, Samuel E. Moelius:
Parallelism Increases Iterative Learning Power. 49-63 - Sanjay Jain, Frank Stephan, Nan Ye:
Prescribed Learning of R.E. Classes. 64-78 - Sanjay Jain, Frank Stephan:
Learning in Friedberg Numberings. 79-93
Complexity Aspects of Learning
- Vitaly Feldman, Shrenik Shah, Neal Wadhwa:
Separating Models of Learning with Faulty Teachers. 94-106 - César Luis Alonso, José Luis Montaña:
Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations. 107-119 - Vikraman Arvind, Johannes Köbler, Wolfgang Lindner:
Parameterized Learnability of k -Juntas and Related Problems. 120-134 - M. M. Hassan Mahmud:
On Universal Transfer Learning. 135-149
Online Learning
- Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári:
Tuning Bandit Algorithms in Stochastic Environments. 150-165 - Jussi Kujala, Tapio Elomaa:
Following the Perturbed Leader to Gamble at Multi-armed Bandits. 166-180 - Steven Busuttil, Yuri Kalnishkan:
Online Regression Competitive with Changing Predictors. 181-195
Unsupervised Learning
- Markus Maier, Matthias Hein, Ulrike von Luxburg:
Cluster Identification in Nearest-Neighbor Graphs. 196-210 - Kevin L. Chang:
Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in \mathbb Rd. 211-226
Language Learning
- Ryo Yoshinaka:
Learning Efficiency of Very Simple Grammars from Positive Data. 227-241 - François Denis, Amaury Habrard:
Learning Rational Stochastic Tree Languages. 242-256
Query Learning
- Sanjay Jain, Efim B. Kinber:
One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples. 257-271 - Cristina Tîrnauca, Timo Knuutila:
Polynomial Time Algorithms for Learning k -Reversible Languages and Pattern Languages with Correction Queries. 272-284 - Lev Reyzin, Nikhil Srivastava:
Learning and Verifying Graphs Using Queries with a Focus on Edge Counting. 285-297 - Rika Okada, Satoshi Matsumoto, Tomoyuki Uchida, Yusuke Suzuki, Takayoshi Shoudai:
Exact Learning of Finite Unions of Graph Patterns from Queries. 298-312
Kernel-Based Learning
- Kilho Shin, Tetsuji Kuboyama:
Polynomial Summaries of Positive Semidefinite Kernels. 313-327 - Guillaume Stempfel, Liva Ralaivola:
Learning Kernel Perceptrons on Noisy Data Using Random Projections. 328-342 - Adam Kowalczyk:
Continuity of Performance Metrics for Thin Feature Maps. 343-357
Other Directions
- Takafumi Kanamori:
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. 358-372 - Ronald Ortner:
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes. 373-387 - Vladimir V. V'yugin:
On Calibration Error of Randomized Forecasting Algorithms. 388-402
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