14.txt

This complete matlab for neural network

发信人: nohau (nohau), 信区: DataMining
标  题: 第七章的几个重要的概念
发信站: 南京大学小百合站 (Thu Jan  2 15:58:28 2003)

列出了第七章的几个重要的概念.
我觉得这些概念是第七章的主要内容,准确的把握这几个概念是下一步学习的基础.
Chapter 7 
Concepts:
7.2 
真实错误率
True error: the true error of hypothesis h with respect to target concept c an
d distribution D is the probability that h will misclassify an instance drawn 
at random according to D.
       
Note:
The true error depend on the unknown distribution D .

训练错误率
Training error: the probability of training examples misclassified by h.

Note:
1.The training error can be observed by the learner directly, and the true err
or can not.
2.The main problem of learning complexity is: “how probability of  the observ
ed training error for h gives a misleading estimate of the true error.”

样本错误率
Sample error:(defined in chapter 5) The sample error of a hypothesis with resp
ect to some sample S of instances drawn from X is the faction of S that is mis
classified. 

Note:
If S is the set of the training data, the sample error is the training error.


可ＰＡＣ学习
Consider some class C of possible target concepts and a learner using hypothes
is space  H. If a learner L conform two things, C is PAC-learnable by L using 
H.: First, L must with arbitrarily high probability (1-d) output a hypothesis 
having arbitrarily low error (e). Second, it must be efficiently, in time that
 grows at most polynomially with 1/e and 1/d.

7.3
样本复杂度
Sample complexity
The growth in the number of  required training examples with problem size, cal
led the complexity of the learning problem.

一致学习器
Consistent learner:
If a learner output hypothesis that perfectly fit the training data, it is cal
led consistent learner. 

e-详尽
e-exhausted: The version space VSH,D is said to e-exhausted with respect to c 
and D, if every hypothesis h in VSH,D has error less than e with respect to c 
and D.


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