精通Java机器学习Uday,Kamath东南SN

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<>基本信息

作者:Uday,Kamath

出版社：东南大学出版社

出版日期：2018-10-01

<>编辑推荐

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<>《精通Java机器学习（影印版）》将为你介绍关于机器学习的一批先技术，包括分类、聚类、异常检测、流学习、主动学习、半监督学习、概率图模型、文本挖掘、深度学习以及大数据批和流机器学习。每章都有说明性的示例和真实案例，展示了如何利用基于Java的工具来运用这些新技术。

<>内容提要

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<>《精通Java机器学习（影印版）》将为你介绍关于机器学习的一批先技术，包括分类、聚类、异常检测、流学习、主动学习、半监督学习、概率图模型、文本挖掘、深度学习以及大数据批和流机器学习。每章都有说明性的示例和真实案例，展示了如何利用基于Java的工具来运用这些新技术。

<>目录

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<>reface
Chater 1: Machine Learning Review
Machine learning - history and definition
What is not machine learning?
Machine learning - concets and terminology
Machine learning - tyes and subtyes
Datasets used imachine learning
Machine learning alications
ractical issues imachine learning
Machine learning - roles and rocess
Roles
rocess
Machine learning -tools and datasets
Datasets
Summary
Chater 2: ractical Aroach to Real-World Suervised Learning
Formal descritioand notation
Data quality analysis
Descritive data analysis
Basic label analysis
Basic feature analysis
Visualizatioanalysis
Univariate feature analysis
Multivariate feature analysis
Data transformatioand rerocessing
Feature construction
Handling missing values
Outliers
Discretization
Data samling
Is samling needed?
Undersamling and oversamling
Training, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniques
Feature evaluatiotechniques
Filter aroach
Wraer aroach
Embedded aroach
Model building
Linear models
Linear Regression
Naive Bayes
Logistic Regression
Non-linear models
DecisioTrees
K-Nearest Neiors (KNN)
Suort vector machines (SVM)
Ensemble learning and meta learners
Bootstra aggregating or bagging
Boosting
Model assessment, evaluation, and arisons
Model assessment
Model evaluatiometrics
Confusiomatrix and related metrics
ROC and RC curves
Gaicharts and lift curves
Model arisons
Comaring two algorithms
Comaring multile algorithms
Case Study - Horse Colic Classification
Business roblem
Machine learning maing
Data analysis
Label analysis
Features analysis
Suervised learning exeriments
Weka exeriments
RaidMiner exeriments
Results, observations, and analysis
Summary
References
Chater 3: Unsuervised Machine Learninq Techniques
Chater 4: Semi-Suervised and Active Learning
Chater 5: Real-Time Stream Machine Learning
Chater 6: robabilistic Grah Modeling
Chater 7: Dee Learning
Chater 8: Text Mining and Natural Language rocessing
Chater 9: Bia Data Machine Learnina - The Final Frontier
Aendix A: Linear Algebra
Aendix B: robability
Index

<>作者介绍

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