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Machine Learning, Optimization, and Big Data

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Cover of 'Machine Learning, Optimization, and Big Data'

Table of Contents

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    Book Overview
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    Chapter 1 Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models
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    Chapter 2 Improving Support Vector Machines Performance Using Local Search
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    Chapter 3 Projective Approximation Based Quasi-Newton Methods
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    Chapter 4 Intra-feature Random Forest Clustering
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    Chapter 5 Dolphin Pod Optimization
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    Chapter 6 Contraction Clustering (Raster)
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    Chapter 7 Deep Statistical Comparison Applied on Quality Indicators to Compare Multi-objective Stochastic Optimization Algorithms
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    Chapter 8 On the Explicit Use of Enzyme-Substrate Reactions in Metabolic Pathway Analysis
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    Chapter 9 A Comparative Study on Term Weighting Schemes for Text Classification
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    Chapter 10 Dual Convergence Estimates for a Family of Greedy Algorithms in Banach Spaces
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    Chapter 11 Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization
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    Chapter 12 A Differential Evolution Algorithm to Develop Strategies for the Iterated Prisoner’s Dilemma
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    Chapter 13 Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter
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    Chapter 14 Forecasting Natural Gas Flows in Large Networks
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    Chapter 15 A Differential Evolution Algorithm to Semivectorial Bilevel Problems
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    Chapter 16 Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces
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    Chapter 17 Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch
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    Chapter 18 Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety
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    Chapter 19 Multi-objective Genetic Algorithm for Interior Lighting Design
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    Chapter 20 An Elementary Approach to the Problem of Column Selection in a Rectangular Matrix
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    Chapter 21 A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S $$^3$$ 3 VMs
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    Chapter 22 A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning
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    Chapter 23 Data-Driven Machine Learning Approach for Predicting Missing Values in Large Data Sets: A Comparison Study
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    Chapter 24 Mineral: Multi-modal Network Representation Learning
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    Chapter 25 Visual Perception of Mixed Homogeneous Textures in Flying Pigeons
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    Chapter 26 Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms
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    Chapter 27 SQG-Differential Evolution for Difficult Optimization Problems under a Tight Function Evaluation Budget
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    Chapter 28 Age and Gender Classification of Tweets Using Convolutional Neural Networks
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    Chapter 29 Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem
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    Chapter 30 A Data Mining Tool for Water Uses Classification Based on Multiple Classifier Systems
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    Chapter 31 Parallelized Preconditioned Model Building Algorithm for Matrix Factorization
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    Chapter 32 A Quantitative Analysis on Required Network Bandwidth for Large-Scale Parallel Machine Learning
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    Chapter 33 Can Differential Evolution Be an Efficient Engine to Optimize Neural Networks?
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    Chapter 34 BRKGA-VNS for Parallel-Batching Scheduling on a Single Machine with Step-Deteriorating Jobs and Release Times
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    Chapter 35 Petersen Graph is Uniformly Most-Reliable
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    Chapter 36 GRASP Heuristics for a Generalized Capacitated Ring Tree Problem
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    Chapter 37 Data-Driven Job Dispatching in HPC Systems
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    Chapter 38 AbstractNet: A Generative Model for High Density Inputs
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    Chapter 39 A Parallel Framework for Multi-Population Cultural Algorithm and Its Applications in TSP
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    Chapter 40 Honey Yield Forecast Using Radial Basis Functions
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    Chapter 41 Graph Fragmentation Problem for Natural Disaster Management
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    Chapter 42 Job Sequencing with One Common and Multiple Secondary Resources: A Problem Motivated from Particle Therapy for Cancer Treatment
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    Chapter 43 Robust Reinforcement Learning with a Stochastic Value Function
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    Chapter 44 Finding Smooth Graphs with Small Independence Numbers
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    Chapter 45 BioHIPI: Biomedical Hadoop Image Processing Interface
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    Chapter 46 Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data
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    Chapter 47 Refining Partial Invalidations for Indexed Algebraic Dynamic Programming
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    Chapter 48 Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data
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    Chapter 49 Detection of Age-Related Changes in Networks of B Cells by Multivariate Time-Series Analysis
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Title
Machine Learning, Optimization, and Big Data
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-72926-8
ISBNs
978-3-31-972925-1, 978-3-31-972926-8
Editors

Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 8%
Student > Bachelor 1 2%
Lecturer > Senior Lecturer 1 2%
Student > Master 1 2%
Unknown 53 87%
Readers by discipline Count As %
Engineering 3 5%
Computer Science 3 5%
Mathematics 1 2%
Unknown 54 89%