Gaurang Panchal

Searching Most Efficient Neural Network Architecture Using Akaike's Information Criterion (AIC)

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. Neural networks are commonly used networks in many engineering applications due to its better generalization property. An ensemble neural network algorithm is proposed based on the Akaike information criterion (AIC). Ecologists have long relied on hypothesis testing to include or exclude variables in models, although the conclusions often depend on the approach used. The advent of methods based on information theory, also known as information-theoretic approaches, has changed the way we look at model selection The Akaike information criterion (AIC) has been successfully used in model selection. It is not easy to decide the optimal size of the neural network because of its strong nonlinearity. We discuss problems with well used information and propose a model selection method.

Performance analysis of classification techniques using different parameters

Data mining technique is the process of analyzing data from different perspectives and summarizing it into useful information. Classification refers to the data mining problem of attempting to predict the category of categorical data by building a model based on some predictor variables. The goal of data classification is to organize and categorize data in distinct classes. It does not require any priori knowledge of the class statistical distribution in data sources. ANN can be trained to distinguish the criteria used to classify and it can do so in a generalized manner allowing successful classification of new inputs not used during training. Back propagation as a training algorithm for ANN works well for classification. This Paper shows the issue of improving the fitness of BPN algorithm and the performance analysis of various classification techniques like Naive Bayes, Bayesian network, Support Vector Machine and GABPN discussed.