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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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Ensemble Classification Methods with Applications in R

An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning Ensemble Classification;

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Data Classification

primary aspects of data classification: Methods: The book first describes common techniques used for classification, including;

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Data Classification

primary aspects of data classification: Methods: The book first describes common techniques used for classification;

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Machine Learning Theoretical Foundations and Practical Applications

algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory;

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Intelligent Learning Systems

for pattern classification. Section 2 focuses on intelligent learning methods and approaches, describing evolutionary learning of concepts;

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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces;

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Condition Monitoring with Vibration Signals

Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Density Ratio Estimation in Machine Learning

to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence;

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Density Ratio Estimation in Machine Learning

to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence;

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Ensemble Machine Learning Cookbook

models using individual recipes. What you will learn Understand how to use machine learning algorithms for regression and classification problems;

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Machine Learning

Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive;

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Advances in Machine Learning Applications in Software Engineering

increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering;

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Data Analytics in Bioinformatics

becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines;

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Machine Learning Research Progress

As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Time Series Clustering and Classification

classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students;

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Advances in Hybridization of Intelligent Methods

interested in using hybrid methods for solving complex 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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Hybrid Image Processing Methods for Medical Image Examination

various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book;

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Machine Learning and Data Mining in Pattern Recognition

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning;

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Mastering Machine Learning with R -

methods such as Multi-Class Classification and Unsupervised Learning * Implement advanced concepts in machine learning with this example-rich;

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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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Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning;

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Optimization in Machine Learning and Applications

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information;

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