Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Robust Distributed Training of Linear Classifiers Based on Divergence Minimization Principle
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   Chapter 2 Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy
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   Chapter 3 Causal Clustering for 2-Factor Measurement Models
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   Chapter 4 Support Vector Machines for Differential Prediction
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   Chapter 5 Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control
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   Chapter 6 Mining Top-K Largest Tiles in a Data Stream
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   Chapter 7 Ranked Tiling
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   Chapter 8 Fast Estimation of the Pattern Frequency Spectrum
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    Chapter 9 Recurrent Greedy Parsing with Neural Networks
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    Chapter 10 FILTA: Better View Discovery from Collections of Clusterings via Filtering
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    Chapter 11 Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes
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    Chapter 12 Learning Binary Codes with Bagging PCA
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    Chapter 13 Conic Multi-task Classification
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    Chapter 14 Bi-directional Representation Learning for Multi-label Classification
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    Chapter 15 Optimal Thresholding of Classifiers to Maximize F1 Measure
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    Chapter 16 Randomized Operating Point Selection in Adversarial Classification
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    Chapter 17 Hierarchical Latent Tree Analysis for Topic Detection
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    Chapter 18 Bayesian Models for Structured Sparse Estimation via Set Cover Prior
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    Chapter 19 Preventing Over-Fitting of Cross-Validation with Kernel Stability
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    Chapter 20 Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach
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    Chapter 21 On the Null Distribution of the Precision and Recall Curve
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    Chapter 22 Linear State-Space Model with Time-Varying Dynamics
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    Chapter 23 An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions
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    Chapter 24 GMRF Estimation under Topological and Spectral Constraints
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    Chapter 25 Rate-Constrained Ranking and the Rate-Weighted AUC
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    Chapter 26 Rate-Oriented Point-Wise Confidence Bounds for ROC Curves
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    Chapter 27 A Fast Method of Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration
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    Chapter 28 Large-Scale Multi-label Text Classification — Revisiting Neural Networks
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    Chapter 29 Distinct Chains for Different Instances: An Effective Strategy for Multi-label Classifier Chains
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    Chapter 30 A Unified Framework for Probabilistic Component Analysis
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    Chapter 31 Flexible Shift-Invariant Locality and Globality Preserving Projections
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    Chapter 32 Interactive Knowledge-Based Kernel PCA
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    Chapter 33 A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction
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    Chapter 34 Deterministic Feature Selection for Regularized Least Squares Classification
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    Chapter 35 Boosted Bellman Residual Minimization Handling Expert Demonstrations
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    Chapter 36 Semi-supervised Learning Using an Unsupervised Atlas
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    Chapter 37 A Lossless Data Reduction for Mining Constrained Patterns in n-ary Relations
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    Chapter 38 Interestingness-Driven Diffusion Process Summarization in Dynamic Networks
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    Chapter 39 Neural Gaussian Conditional Random Fields
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    Chapter 40 Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees
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    Chapter 41 Boosted Mean Shift Clustering
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    Chapter 42 Hypernode Graphs for Spectral Learning on Binary Relations over Sets
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    Chapter 43 Discovering Dynamic Communities in Interaction Networks
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    Chapter 44 Anti-discrimination Analysis Using Privacy Attack Strategies