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Advances in Classification and Data Analysis

This volume contains a selection of papers presented at the biannual meeting of the Classification and Data Analysis Group of Societa;

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Data Classification

, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification;

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Data Classification

, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification;

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Classification and Data Analysis

This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the;

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Landcover Classification and Topography

Mapper data (TM) to test the effect of topography on landcover classification for two different landcover classification legends.;

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Genetic Algorithms, Fuzzy Systems & Website Classification

; classification algorithms in handling noisy training data and meta data generation for automates web page classification.;

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Data Science

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Data Complexity in Pattern Recognition

Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its;

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Data Complexity in Pattern Recognition

Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its;

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Classification

develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification;

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Classification Methods for Remotely Sensed Data

Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has;

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Data Analysis and Classification

of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the;

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Clustering Methodology for Symbolic Data

in order to extract knowledge from such a data. Classification Methodology for Symbolic Data: * Provides new classification methodologies for;

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Model-Based Clustering and Classification for Data Science

, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering;

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Classification

develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification;

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Classification, (Big) Data Analysis and Statistical Learning

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data;

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Sequence Classification Using Support Vector Machines

This book is about classification of nucleotide chains from yeast metabolic cycle data. First of all, the goals and the data set will be;

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Machine Learning Pocket Reference Working with Structured Data in Python

learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value;

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply;

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Development of Two Hybrid Classification Methods for Machine Learning

In this work two studies are done and they are referred as first study which is named "A Hybrid Classification Method Using Bayesian, K;

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Learning from Imbalanced Data Sets

distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to;

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Learning from Imbalanced Data Sets

distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to;

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Satellite Image Analysis Clustering and Classification

information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error;

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Ensemble decision trees

This book concentrates on the problem of credit assignment. The goal of this work is to propose ensemble classification method, which can;

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Heterometallic Coordination Copper (Ii) Compounds

Heterometallic Coordination Copper (Ii) Compounds - Classification & Analysis of Crystallographic & Structural Data;

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Advanced Analysis and Learning on Temporal Data

classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal;

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