Abstract is missing.
- Editors IntroductionMarcus Hutter, Rocco A. Servedio, Eiji Takimoto. 1-8 [doi]
- A Theory of Similarity Functions for Learning and ClusteringAvrim Blum. 9 [doi]
- Machine Learning in Ecosystem InformaticsThomas G. Dietterich. 10-11 [doi]
- Challenge for Info-plosionMasaru Kitsuregawa. 12 [doi]
- A Hilbert Space Embedding for DistributionsAlex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf. 13-31 [doi]
- Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and CreativityJürgen Schmidhuber. 32-33 [doi]
- Feasible Iteration of Feasible Learning FunctionalsJohn Case, Timo Kötzing, Todd Paddock. 34-48 [doi]
- Parallelism Increases Iterative Learning PowerJohn Case, Samuel E. Moelius. 49-63 [doi]
- Prescribed Learning of R.E. ClassesSanjay Jain, Frank Stephan, Ye Nan. 64-78 [doi]
- Learning in Friedberg NumberingsSanjay Jain, Frank Stephan. 79-93 [doi]
- Separating Models of Learning with Faulty TeachersVitaly Feldman, Shrenik Shah, Neal Wadhwa. 94-106 [doi]
- Vapnik-Chervonenkis Dimension of Parallel Arithmetic ComputationsCésar Luis Alonso, José Luis Montaña. 107-119 [doi]
- Parameterized Learnability of ::::k:::: -Juntas and Related ProblemsVikraman Arvind, Johannes Köbler, Wolfgang Lindner. 120-134 [doi]
- On Universal Transfer LearningM. M. Hassan Mahmud. 135-149 [doi]
- Tuning Bandit Algorithms in Stochastic EnvironmentsJean-Yves Audibert, Rémi Munos, Csaba Szepesvári. 150-165 [doi]
- Following the Perturbed Leader to Gamble at Multi-armed BanditsJussi Kujala, Tapio Elomaa. 166-180 [doi]
- Online Regression Competitive with Changing PredictorsSteven Busuttil, Yuri Kalnishkan. 181-195 [doi]
- Cluster Identification in Nearest-Neighbor GraphsMarkus Maier, Matthias Hein, Ulrike von Luxburg. 196-210 [doi]
- Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in /mathbb ::::R:::::::::::d:::::::Kevin L. Chang. 211-226 [doi]
- Learning Efficiency of Very Simple Grammars from Positive DataRyo Yoshinaka. 227-241 [doi]
- Learning Rational Stochastic Tree LanguagesFrançois Denis, Amaury Habrard. 242-256 [doi]
- One-Shot Learners Using Negative Counterexamples and Nearest Positive ExamplesSanjay Jain, Efim B. Kinber. 257-271 [doi]
- Polynomial Time Algorithms for Learning ::::k:::: -Reversible Languages and Pattern Languages with Correction QueriesCristina Tîrnauca, Timo Knuutila. 272-284 [doi]
- Learning and Verifying Graphs Using Queries with a Focus on Edge CountingLev Reyzin, Nikhil Srivastava. 285-297 [doi]
- Exact Learning of Finite Unions of Graph Patterns from QueriesRika Okada, Satoshi Matsumoto, Tomoyuki Uchida, Yusuke Suzuki, Takayoshi Shoudai. 298-312 [doi]
- Polynomial Summaries of Positive Semidefinite KernelsKilho Shin, Tetsuji Kuboyama. 313-327 [doi]
- Learning Kernel Perceptrons on Noisy Data Using Random ProjectionsGuillaume Stempfel, Liva Ralaivola. 328-342 [doi]
- Continuity of Performance Metrics for Thin Feature MapsAdam Kowalczyk. 343-357 [doi]
- Multiclass Boosting Algorithms for Shrinkage Estimators of Class ProbabilityTakafumi Kanamori. 358-372 [doi]
- Pseudometrics for State Aggregation in Average Reward Markov Decision ProcessesRonald Ortner. 373-387 [doi]
- On Calibration Error of Randomized Forecasting AlgorithmsVladimir V. V yugin. 388-402 [doi]