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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Data Split Strategiesfor Evolving Predictive Models
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    Chapter 2 Discriminative Interpolation for Classification of Functional Data
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    Chapter 3 Fast Label Embeddings via Randomized Linear Algebra
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    Chapter 4 Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation
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    Chapter 5 Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration
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    Chapter 6 Parameter Learning of Bayesian Network Classifiers Under Computational Constraints
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    Chapter 7 Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning
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    Chapter 8 Regression with Linear Factored Functions
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    Chapter 9 Ridge Regression, Hubness, and Zero-Shot Learning
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    Chapter 10 Solving Prediction Games with Parallel Batch Gradient Descent
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    Chapter 11 Structured Regularizer for Neural Higher-Order Sequence Models
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    Chapter 12 Versatile Decision Trees for Learning Over Multiple Contexts
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    Chapter 13 When is Undersampling Effective in Unbalanced Classification Tasks?
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    Chapter 14 A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons
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    Chapter 15 Bayesian Active Clustering with Pairwise Constraints
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    Chapter 16 ConDist: A Context-Driven Categorical Distance Measure
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    Chapter 17 Discovering Opinion Spammer Groups by Network Footprints
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    Chapter 18 Gamma Process Poisson Factorization for Joint Modeling of Network and Documents
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    Chapter 19 Generalization in Unsupervised Learning
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    Chapter 20 Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with $$L_{2,1}$$ Regularization
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    Chapter 21 Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation
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    Chapter 22 Machine Learning and Knowledge Discovery in Databases
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    Chapter 23 Multi-view Semantic Learning for Data Representation
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    Chapter 24 Unsupervised Feature Analysis with Class Margin Optimization
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    Chapter 25 Ageing-Based Multinomial Naive Bayes Classifiers Over Opinionated Data Streams
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    Chapter 26 Drift Detection Using Stream Volatility
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    Chapter 27 Early Classification of Time Series as a Non Myopic Sequential Decision Making Problem
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    Chapter 28 Ising Bandits with Side Information
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    Chapter 29 Refined Algorithms for Infinitely Many-Armed Bandits with Deterministic Rewards
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    Chapter 30 An Empirical Investigation of Minimum Probability Flow Learning Under Different Connectivity Patterns
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    Chapter 31 Difference Target Propagation
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    Chapter 32 Online Learning of Deep Hybrid Architectures for Semi-supervised Categorization
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    Chapter 33 Scoring and Classifying with Gated Auto-Encoders
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    Chapter 34 Sign Constrained Rectifier Networks with Applications to Pattern Decompositions
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    Chapter 35 Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets
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    Chapter 36 Higher Order Fused Regularization for Supervised Learning with Grouped Parameters
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    Chapter 37 Joint Semi-supervised Similarity Learning for Linear Classification
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    Chapter 38 Learning Compact and Effective Distance Metrics with Diversity Regularization
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    Chapter 39 Scalable Metric Learning for Co-Embedding
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    Chapter 40 Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems
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    Chapter 41 Hash Function Learning via Codewords
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    Chapter 42 HierCost: Improving Large Scale Hierarchical Classification with Cost Sensitive Learning
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    Chapter 43 Large Scale Optimization with Proximal Stochastic Newton-Type Gradient Descent
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    Chapter 44 Erratum to: Bayesian Active Clustering with Pairwise Constraints
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    Chapter 45 Erratum to: Scalable Metric Learning for Co-Embedding
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    Chapter 46 Erratum to: Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning
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Title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-23528-8
ISBNs
978-3-31-923527-1, 978-3-31-923528-8
Editors

Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Canada 1 1%
Germany 1 1%
China 1 1%
Algeria 1 1%
Unknown 83 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 27%
Student > Master 21 23%
Researcher 15 16%
Student > Bachelor 6 7%
Other 4 4%
Other 12 13%
Unknown 8 9%
Readers by discipline Count As %
Computer Science 51 56%
Engineering 7 8%
Neuroscience 6 7%
Mathematics 4 4%
Agricultural and Biological Sciences 3 3%
Other 7 8%
Unknown 13 14%