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.