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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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High-dimensional Data Analysis

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide;

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A Hybrid Feature Selection Model For Genome Wide Association Studies

- high dimensional data and relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high;

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Spectral Feature Selection for Data Mining

dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems;

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Spectral Feature Selection for Data Mining

dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems;

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Hybrid Methods in Feature Selection

selection is essential for the machine learning algorithms while handling high dimensional datasets. Many traditional search methods have shown;

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Computational Methods of Feature Selection

selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable;

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Statistical Foundations of Data Science

Provides theoretical insights and justification of the statistical procedures for the analysis of high-dimensional data Presents a general;

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Perspectives on Big Data Analysis

estimation for an array of interesting models. The book also presents some surprising results on high-dimensional data analysis, especially when;

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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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High Dimensional Econometrics and Identification

recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the;

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Big Data in Omics and Imaging: Association Analysis

advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model;

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Big Data in Omics and Imaging: Association Analysis

advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and;

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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

depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive;

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Data Science

. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and;

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Big Data in Omics and Imaging, Two Volume Set

analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching;

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Dependence Modeling with Copulas

vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops;

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Dependence Modeling with Copulas

vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops;

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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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Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data

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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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Principles and Methods for Data Science

Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics;

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Deep Learning and Missing Data in Engineering Systems

estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical;

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Data Clustering

, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as;

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Foundations of Large-Scale Multimedia Information Management and Retrieval

retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring;

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