It is a very simple project for someone who knows matlab and pattern recognition. I have 3 types of data Data_1, Data_2,and Data_3. So the training set are 5 pattern for data_1, 10 pattern for Data_2 and Data_3 and the width of the pattern 16 output. Consequently the total of training is 25 patterns
The testing set are 20 pattern for Data_1 and 40 for Data_2 and Data_3, the total is 100 patterns
data sets=25 patterns + 100 patterns= 125
I need the identification performance using K-nearest neighbor (KNN), Multi-layer perception (MLP), Radial Basis Function (RBF), Gaussian Mixture Models (GMM) and Probabilistic Principal Component Analysis (PPCA).
Also identification results based on data sets with different principal components in this work(%). for PPCA, KNN, MLP, RBF and GMM
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I am an IIT graduate ([url removed, login to view],[url removed, login to view]), and have done several such projects here, the most recent one being for hdogan directly on neural network pattern recognition. Apart from myself, I also have people with good knowl Lisää