Machine Learning
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A01=Eihab Bashier Mohammed Bashier
A01=Mohssen Mohammed
A01=Muhammad Badruddin Khan
Author_Eihab Bashier Mohammed Bashier
Author_Mohssen Mohammed
Author_Muhammad Badruddin Khan
Average Income
Bayesian inference
Category=UMB
Category=UYQM
Covariance Matrix
Covariance Matrix Calculation
Dataset D1
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Expectation Maximization Algorithm
Feature Vectors
Fisher Linear Discriminant
Gaussian Mixture Model
Hidden Markov Model
hidden Markov models
Linear Discriminant Analysis
machine learning
machine learning algorithms
MATLAB Environment
MATLAB's Code
MATLAB’s Code
Multilayer Perceptron Network
Multilayer Perceptron Neural Network
Optimal Lagrange Multipliers
Parent Dataset
Polymorphic Worm
Posterior Probability
practical algorithm implementation guide
principal component analysis
Pruning Rules
QP Problem
smart machines
Smart Phones
string matching techniques
supervised classification
Support Vector Machine
SVM Method
SVM Model
unsupervised clustering
Worm Signature
Product details
- ISBN 9781498705387
- Weight: 500g
- Dimensions: 156 x 234mm
- Publication Date: 18 Jul 2016
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier
