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This paper describes the Bangla Document Categorization using Stochastic Gradient Descent (SGD) classifier. Here, document categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. The proposed system can be divided into three steps: 1. feature extraction incorporating term frequency (TF) and inverse document frequency (IDF), 2. classifier design using the Stochastic Gradient Descent (SGD) algorithm by learning the distinct features, and 3. performance measure using F1-score. In the experiments on BDNews24 documents, it is observed that our proposed method provides higher accuracy in comparison with the methods based on Support Vector Machine (SVM) and Naive Bayesian (NB) classifier.

SUMMARYThis paper describes a distinctive phonetic feature (DPF) extraction method for use in a phoneme recognition system; our method has a low computation cost. This method comprises three stages. The first stage uses two multilayer neural networks (MLNs): MLN LF−DPF , which maps continuous acoustic features, or local features (LFs), onto discrete DPF features, and MLN Dyn , which constrains the DPF context at the phoneme boundaries. The second stage incorporates inhibition/enhancement (In/En) functionalities to discriminate whether the DPF dynamic patterns of trajectories are convex or concave, where convex patterns are enhanced and concave patterns are inhibited. The third stage decorrelates the DPF vectors using the Gram-Schmidt orthogonalization procedure before feeding them into a hidden Markov model (HMM)-based classifier. In an experiment on Japanese Newspaper Article Sentences (JNAS) utterances, the proposed feature extractor, which incorporates two MLNs and an In/En network, was found to provide a higher phoneme correct rate with fewer mixture components in the HMMs. key words: distinctive phonetic feature, hidden Markov model, multilayer neural network, inhibition/enhancement network, local features

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