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Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying;

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Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying;

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Feature Selection in Data Mining - Approaches Based on Information Theory

set of features will almost certainly overfit on future data sets. The book presents streamwise feature selection which interleaves the;

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Hierarchical Feature Selection for Knowledge Discovery

discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different;

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Feature Engineering for Machine Learning and Data Analytics

data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation;

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Feature Engineering for Machine Learning and Data Analytics

as a supplement for courses on machine learning, data mining, and big data analytics.;

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Artificial Intelligence and Data Mining in Healthcare

compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science;

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Feature Selection for High-Dimensional Data

foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis;

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Computational Methods of Feature Selection

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields;

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Advances in Knowledge Discovery and Data Mining

; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms;

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Advances in Knowledge Discovery and Data Mining

; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms;

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Advances in Knowledge Discovery and Data Mining

uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional, and;

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Advances in Knowledge Discovery and Data Mining

uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional and;

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Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a;

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Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a;

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A Hybrid Feature Selection Model For Genome Wide Association Studies

dimensional data and to be able to find the nonlinear relations, data mining approaches are needed. In this work, a hybrid feature selection model;

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Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both;

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Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and feature selection; representation learning and embedding; mining;

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Advances in Knowledge Discovery and Data Mining

learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors;

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Advances in Knowledge Discovery and Data Mining

learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors;

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Solving Large Scale Learning Tasks. Challenges and Algorithms

theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms;

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Better Decision Tree from Intelligent Instance Selection

improve the quality of the data mining results. This book presents a new optimization-based approach for instance selection that uses a genetic;

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Mathematical Analysis For Machine Learning And Data Mining

This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The;

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Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par

science: applications; demo track. Chapter Incorporating Dependencies in Spectral Kernels for Gaussian Processes is available open access;

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Data Mining Algorithms

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for;

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Mobile Data Mining and Applications

, from a mobile data mining platform for collecting mobile data, to mobile data processing, and mobile feature discovery. Usage of mobile data;

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