目录

• ch1 machine learning? where to use?application example.

 more examples of fielded applications

• ch2  input :concept instance attribute 输入

sparse data,string and date attribute

• ch3 output--knowledge representation different representation to different algorithms 输出

interactive decision tree

• ch4 basic methods of machine learning 算法基本思想

multinominal Bayes models for document classification,logistic regression

• ch5 performence evaluation

Kappa statistic for measuring the  success of a predictor   ；cost-sensitive learning bulid a cost-sensitive model  ;cost curves

• ch6 machine learning algorithms

NN ; Bayesin network classifiers;heuristics used in the successful RIPPER rule learner;model tree  to rules for numeric prediction ; apply locally weighted regression to classification problems;X-mean clustering algorithm

• ch7 practical topics 属性选择、离散化

new attribute selection schemes such as race search and
the use of support vector machines and new methods for combining models
such as additive regression, additive logistic regression, logistic model trees, and
option trees;LogitBoost ; useful transformations (principal components analysis and transformations for text mining and time series); using unlabeled
data to improve classification( co-training and co-EM methods).