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Algorithms for Regression and Classification

The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;

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Data Mining Algorithms

building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality;

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Support Vector Machines

of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization;

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Machine Learning with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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Data Mining and Machine Learning

for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and;

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Machine Learning Foundations

computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation;

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Assessing and Improving Prediction and Classification

using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification;

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Evolutionary Machine Learning Techniques

methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning;

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Evolutionary Machine Learning Techniques

methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning;

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Machine Learning with Scala Quick Start Guide

covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning;

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Data Science Algorithms in a Week

how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What;

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Data Mining Concept Animation Library

Library. Potential users for this library can be future students and instructor of a data mining class. The various types of data mining;

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Pattern Classification

Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.;

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Hands-On Machine Learning with ML.NET

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity;

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Data Science for Marketing Analytics

understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification;

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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 Algorithms in a Week

understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. Style;

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Predictive Analytics

Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive;

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Machine Learning Algorithms

for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that;

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Introduction to Machine Learning with R

regression models used in business and science Use single-layer and multilayer neural networks for calculating outcomes Look at how tree-based;

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Deep Learning Applications

classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to;

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Advances in Classification and Data Analysis

analysis Regression Models for Data Analysis Nonparametric methods SPATIAL AND TIME SERIES DATA ANALYSIS Time series analysis Spatial data analysis;

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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 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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Statistical Regression and Classification

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned;

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Artificial Intelligence and Machine Learning Fundamentals

regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out;

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Go Machine Learning Projects

learning ecosystem Implement clustering, regression, classification, and neural networks with Go Get to grips with libraries such as Gorgonia;

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