Data Mining Algorithms

Explained Using R

Pawel Cichosz

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Idioma:English
ISBN:9781118950807
Fecha de lanzamiento:19 nov 2014
Editorial:Wiley

Edición:Wiley
Formato de archivo:EPUB3
Tamaño de archivo:21.33 MB
Opciones de descarga:EPUB3 (Adobe DRM)

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Data Mining Algorithms

¿Por qué leer con nosotros?

Sinopsis

Acerca de este libro

Detalles del libro

Cómo leer

Lecturas relacionadas

The Drunkard's Walk

Introductory R: A Beginner's Guide to Data Visualisation, Statistical Analysis and Programming in R

Statistics For Dummies

A Gentle Introduction to Optimization

Counterfactuals and Causal Inference

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Applied Survival Analysis

Survival Analysis Using SAS

Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques

Probability And Statistical Theory For Applied Researchers

The Trend Management Toolkit

Causality

Probability for Statistics and Machine Learning

A First Course in Probability and Markov Chains

The Maths Behind...

Applied Statistics in Biomedicine and Clinical Trials Design

Probabilistic Diophantine Approximation

Indirect Questioning in Sample Surveys

Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance

Statistical Analysis and Data Display

Population Issues

Social Media, Sociality, and Survey Research

Preventing and Treating Missing Data in Longitudinal Clinical Trials

Nonlinear Stochastic Systems with Incomplete Information

Illuminating Statistical Analysis Using Scenarios and Simulations

Random Matrices And Random Partitions: Normal Convergence

Stochastic Differential Equations, Backward SDEs, Partial Differential Equations

Stochastic Analysis and Diffusion Processes

Applied Nonparametric Statistics in Reliability

Superconcentration and Related Topics

geoENV VII – Geostatistics for Environmental Applications

Stable Non-Gaussian Self-Similar Processes with Stationary Increments

The Conduct of a Cooperative Clinical Trial

Computational Probability

CEMAF as a Census Method

Nonparametric Finance

Quantitative Analysis and IBM® SPSS® Statistics

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