Support vector machine is an important and fundamental technique in machine learning. In
this paper, we apply a semismooth Newton method to solve two typical SVM models: the L2 …

Weighting and normalization are the most important factor that may affect the text
representation significantly. This paper presents two novel term weighting schemes to …

High Relevance Keyword Extraction (HRKE) facility is introduced to Bayesian text
classification to perform feature/keyword extraction during the classifying stage, without …

Text classification is enduring to be one of the most researched problems due to
continuously-increasing amount of electronic documents and digital data. Naive Bayes is an …

In this paper, we present Latent Drichlet Allocation in automatic text summarization to
improve accuracy in document clustering. The experiments involving 398 data set from …

Increased accuracy in predictive models for handwritten character recognition will open up
new frontiers for optical character recognition. Major drawbacks of predictive machine …

In this article, a new initial centroid selection for a K-means document clustering algorithm,
namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K …

Multioutput regression aims to predict multiple continuous outputs simultaneously using the
common set of input variables. The significant challenge arises from modeling relevance …

This paper proposes an automatic mood detection of music with a composition of transfer
learning and multilayer. The five layered convolutional neural network pre-trained on Million …

Document clustering is the process of segmenting a particular collection of texts into
subgroups including content based similar ones. The purpose of document clustering is to …