Formulae for trainable parameter counts are developed for a few popular layers as function of layer parameters and input characteristics. The results are then reconciled with what Keras reports upon running the model…
SVM with Tf-idf vectors edges out LSTM in quality and performance for classifying the 20-newsgroups text corpus.
For generic text, word bag approaches are very efficient at text classification. For a binary text classification task studied here, LSTM working with word sequences is on par in quality with SVM using tf-idf vectors. But performance is a different matter…
Sequence respecting approaches have an edge over bag-of-words implementations when the said sequence is material to classification. Long Short Term Memory (LSTM) neural nets with words sequences are evaluated against Naive Bayes with tf-idf vectors on a synthetic text corpus for classification effectiveness.
