Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning
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   Chapter 2 Learning from Demonstrations: Is It Worth Estimating a Reward Function?
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   Chapter 3 Recognition of Agents Based on Observation of Their Sequential Behavior
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   Chapter 4 Learning Throttle Valve Control Using Policy Search
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   Chapter 5 Model-Selection for Non-parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System
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   Chapter 6 Learning Graph-Based Representations for Continuous Reinforcement Learning Domains
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   Chapter 7 Regret Bounds for Reinforcement Learning with Policy Advice
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   Chapter 8 Exploiting Multi-step Sample Trajectories for Approximate Value Iteration
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    Chapter 9 Expectation Maximization for Average Reward Decentralized POMDPs
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    Chapter 10 Properly Acting under Partial Observability with Action Feasibility Constraints
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    Chapter 11 Iterative Model Refinement of Recommender MDPs Based on Expert Feedback
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    Chapter 12 Solving Relational MDPs with Exogenous Events and Additive Rewards
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    Chapter 13 Continuous Upper Confidence Trees with Polynomial Exploration – Consistency
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    Chapter 14 A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
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    Chapter 15 Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
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    Chapter 16 Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization
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    Chapter 17 A Time and Space Efficient Algorithm for Contextual Linear Bandits
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    Chapter 18 Knowledge Transfer for Multi-labeler Active Learning
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    Chapter 19 Spectral Learning of Sequence Taggers over Continuous Sequences
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    Chapter 20 Fast Variational Bayesian Linear State-Space Model
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    Chapter 21 Machine Learning and Knowledge Discovery in Databases
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    Chapter 22 Explaining Interval Sequences by Randomization
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    Chapter 23 Itemset Based Sequence Classification
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    Chapter 24 A Relevance Criterion for Sequential Patterns
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    Chapter 25 A Fast and Simple Method for Mining Subsequences with Surprising Event Counts
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    Chapter 26 Relevant Subsequence Detection with Sparse Dictionary Learning
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    Chapter 27 Future Locations Prediction with Uncertain Data
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    Chapter 28 Modeling Short-Term Energy Load with Continuous Conditional Random Fields
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    Chapter 29 Fault Tolerant Regression for Sensor Data
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    Chapter 30 Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them
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    Chapter 31 Adaptive Model Rules from Data Streams
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    Chapter 32 Fast and Exact Mining of Probabilistic Data Streams
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    Chapter 33 Detecting Bicliques in GF[q]
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    Chapter 34 As Strong as the Weakest Link:Mining Diverse Cliques in Weighted Graphs
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    Chapter 35 How Robust Is the Core of a Network?
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    Chapter 36 Community Distribution Outlier Detection in Heterogeneous Information Networks
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    Chapter 37 Protein Function Prediction Using Dependence Maximization
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    Chapter 38 Improving Relational Classification Using Link Prediction Techniques
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    Chapter 39 A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data
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    Chapter 40 Efficient Frequent Connected Induced Subgraph Mining in Graphs of Bounded Tree-Width
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    Chapter 41 Continuous Similarity Computation over Streaming Graphs
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    Chapter 42 Trend Mining in Dynamic Attributed Graphs
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    Chapter 43 Sparse Relational Topic Models for Document Networks