Over fitting considerations - artificial intelligence, Computer Engineering

Over fitting Considerations - artificial intelligence

Left  unexamined ,  back  propagation  in  multi-layer  networks  may  be very susceptible  to over fitting itself to the training examples. The following graph plots the error on the training and test set as the number of weight updates increases. It is error prone of networks left to train unchecked.

810_Over fitting Considerations.png

Alarmingly, even though the error on the training set continues to slowly decrease, the error on the test set essentially begins to increase towards the end. It is clearly over fitting, and it relates to the network starting to find and fine-tune to idiosyncrasies in the data, rather than to general properties. Given this phenomena, it would not be wise to use some sort of threshold for the error as the termination condition for back propagation.

In the cases where the number of training examples is high, one antidote to over fitting is to crack the training examples into a set to use to train the weight and a set to hold back as an internal validation set. This is a mini-test set, which may be used to keep the network in check: if the error on the validation set reaches minima and then start to increase, then it could be over fitting in beginning to occur.

Note that (time permitting) it is good giving the training algorithm the advantage of the doubt as much as possible. That is, in the validation set, the error may also go through local minima, and it is unwise to stop training as soon as the validation set error begin to increase, as a better minima can be achieved later on. Of course, if the minima are never bettered, then the network which is in final presented by the learning algorithm should be re-wound to be the 1 which produced the minimum on the validation set.

Another way around over fitting is to decrease each weight by a little weight decay factor during each epoch. Learned networks with large (negative or positive) weights tend to have over fitted the data, because larger weights are needed to accommodate outliers in the data. Thus, keeping the weights low with a weight decay factor can help to steer the network from over fitting.

Posted Date: 10/2/2012 6:39:54 AM | Location : United States







Related Discussions:- Over fitting considerations - artificial intelligence, Assignment Help, Ask Question on Over fitting considerations - artificial intelligence, Get Answer, Expert's Help, Over fitting considerations - artificial intelligence Discussions

Write discussion on Over fitting considerations - artificial intelligence
Your posts are moderated
Related Questions
Differentiate between validation and exception testing. - Validation testing is done to test software in conformance to requirements specified. It aims to demonstrate that soft

Latch is a D- type flip-flop used as a temporary storage device controlled by a timing signal, which can kept 0 or 1. The primary function of a Latch is data storage. It is used in

Storage Technology: In the previous section, the, recent innovations relating to the processing aspects of computer technology were discussed briefly. In considering some of t

how can get payment

XML is the Extensible Markup Language. It betters the functionality of the Web by letting you recognize your information in a more accurate, flexible, and adaptable way. It is e

PROCEDURE TO CREATE PROJECT IN COLLABORATION SYSTEM

Explain Session Layer of OSI Model. The session layer manages, establishes and terminates communication sessions. Communication sessions contain service of requests and serv

Q. Working of Read-Only Memory? A ROM is fundamentally a memory or storage device in which a fixed set of binary information is stored. A block diagram of ROM is as displayed i

A three stage network is designed with the following parameters: M=N=512, p = q = 16 and α = 0.65. Calculate the blocking probability of the network, if s=16. Symbols carry th

Fixed Arithmetic pipelines  We obtain the example of multiplication of fixed numbers. The Two fixed-point numbers are added by the ALU using shift and add operations. This sequ