18. ALT 2007: Sendai, Japan

• 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
  
• Alex 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 R^d. 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