This data set was generated as follows. 150 subjects spoke the name of each letter of the alphabet twice. Hence, we have 52 training examples from each speaker.
A data frame with 900 rows and 618 variables
To reduce package size, only the 3 first letters are included here. The full dataset can be obtained from http://archive.ics.uci.edu/ml/datasets/ISOLET.
The features are described in the paper by Cole and Fanty cited below. The features include spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features. Exact order of appearance of the features is not known.
Dataset downloaded from the UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets/ISOLET
Ron Cole and Mark Fanty Department of Computer Science and Engineering, Oregon Graduate Institute, Beaverton, OR 97006. cole 'at' cse.ogi.edu, fanty 'at' cse.ogi.edu
Tom Dietterich Department of Computer Science Oregon State University, Corvallis, OR 97331 tgd 'at' cs.orst.edu
Fanty, M., Cole, R. (1991). Spoken letter recognition. In Lippman, R. P., Moody, J., and Touretzky, D. S. (Eds). Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann. [http://rexa.info/paper/bee6de062d0d168c5c5b5290b11cd6b12ca8472e]
1 2 3 4 5 6 7 8
# NB: 50 iterations isn't enough in this case, # it was chosen so that the example runs fast enough on CRAN check farm data(UCI.ISOLET.ABC); X=as.matrix(sN.normalizeDF(as.data.frame(UCI.ISOLET.ABC[,1:617]))); y=as.matrix(UCI.ISOLET.ABC[,618]-1); myMLP=sN.MLPtrain(X=X,y=y,hidden_layer_size=20,it=50,lambda=0.5,alpha=0.5); myPrediction=sN.MLPpredict(nnModel=myMLP,X=X,raw=FALSE); table(y,myPrediction);
myPrediction y 0 1 2 0 298 2 0 1 13 286 1 2 0 0 300
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