Machine Learning, Optimization, and Data Science

Table of Contents

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   Book Overview
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   Chapter 1 Deep Neural Network Ensembles
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   Chapter 2 Driver Distraction Detection Using Deep Neural Network
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   Chapter 3 Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays
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   Chapter 4 An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks
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   Chapter 5 Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks
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   Chapter 6 Quantitative and Ontology-Based Comparison of Explanations for Image Classification
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   Chapter 7 About Generative Aspects of Variational Autoencoders
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   Chapter 8 Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks
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    Chapter 9 Incoherent Submatrix Selection via Approximate Independence Sets in Scalar Product Graphs
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    Chapter 10 LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning
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    Chapter 11 Relationship Estimation Metrics for Binary SoC Data
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    Chapter 12 Network Alignment Using Graphlet Signature and High Order Proximity
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    Chapter 13 Effect of Market Spread Over Reinforcement Learning Based Market Maker
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    Chapter 14 A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation
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    Chapter 15 An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem
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    Chapter 16 The Measure of Regular Relations Recognition Applied to the Supervised Classification Task
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    Chapter 17 Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets
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    Chapter 18 Analyses of Multi-collection Corpora via Compound Topic Modeling
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    Chapter 19 Text Mining with Constrained Tensor Decomposition
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    Chapter 20 The Induction Problem: A Machine Learning Vindication Argument
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    Chapter 21 Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree
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    Chapter 22 On Probabilistic k -Richness of the k -Means Algorithms
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    Chapter 23 Using Clustering for Supervised Feature Selection to Detect Relevant Features
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    Chapter 24 A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space
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    Chapter 25 Modification of the k -MXT Algorithm and Its Application to the Geotagged Data Clustering
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    Chapter 26 CoPASample: A Heuristics Based Covariance Preserving Data Augmentation
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    Chapter 27 Active Matrix Completion for Algorithm Selection
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    Chapter 28 A Framework for Multi-fidelity Modeling in Global Optimization Approaches
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    Chapter 29 Performance Evaluation of Local Surrogate Models in Bilevel Optimization
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    Chapter 30 BowTie - A Deep Learning Feedforward Neural Network for Sentiment Analysis
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    Chapter 31 To What Extent Can Text Classification Help with Making Inferences About Students’ Understanding
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    Chapter 32 Combinatorial Learning in Traffic Management
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    Chapter 33 Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits
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    Chapter 34 Designing an Optimal and Resilient iBGP Overlay with Extended ORRTD
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    Chapter 35 GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem
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    Chapter 36 Uniformly Most-Reliable Graphs and Antiholes
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    Chapter 37 Merging Quality Estimation for Binary Decision Diagrams with Binary Classifiers
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    Chapter 38 Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information
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    Chapter 39 Learning Scale and Shift-Invariant Dictionary for Sparse Representation
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    Chapter 40 Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation
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    Chapter 41 Parameter Optimization of Polynomial Kernel SVM from miniCV
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    Chapter 42 Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool
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    Chapter 43 A New Baseline for Automated Hyper-Parameter Optimization
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    Chapter 44 Optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel Data Model
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    Chapter 45 Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City
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    Chapter 46 Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study
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    Chapter 47 A Chained Neural Network Model for Photovoltaic Power Forecast
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    Chapter 48 Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels
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    Chapter 49 Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size
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    Chapter 50 Multi-task Learning by Pareto Optimality
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    Chapter 51 Vital Prognosis of Patients in Intensive Care Units Using an Ensemble of Bayesian Classifiers
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    Chapter 52 On the Role of Hub and Orphan Genes in the Diagnosis of Breast Invasive Carcinoma
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    Chapter 53 Approximating Probabilistic Constraints for Surgery Scheduling Using Neural Networks
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    Chapter 54 Determining Principal Component Cardinality Through the Principle of Minimum Description Length
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    Chapter 55 Modelling Chaotic Time Series Using Recursive Deep Self-organising Neural Networks
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    Chapter 56 On Tree-Based Methods for Similarity Learning
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    Chapter 57 Active Learning Approach for Safe Process Parameter Tuning
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    Chapter 58 Federated Learning of Deep Neural Decision Forests
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    Chapter 59 Conditional Anomaly Detection for Quality and Productivity Improvement of Electronics Manufacturing Systems
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    Chapter 60 Data Anonymization for Privacy Aware Machine Learning
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    Chapter 61 Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications
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    Chapter 62 Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions
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    Chapter 63 Zero-Shot Fashion Products Clustering on Social Image Streams
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    Chapter 64 Treating Artificial Neural Net Training as a Nonsmooth Global Optimization Problem