Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. It is particularly useful when labeled data is scarce. Cotraining is a widely adopted semi-supervised approach that assumes availability of two views of the training data a restrictive assumption for most real world tasks. In this paper, we propose a one-vie...

The exploration of how human beings react to the world and interact with it and each other remains one of the greatest scientific challenges. The ability to recognize affective states of a person we face is the core of emotional intelligence. In the past, several classifiers were adopted independently and tested on several emotional speech corpora with different language, size, number of emotio...

Data Mining in non-stationary data streams is particularly relevant in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) ...

process of machine learning and data mining when certain values are missed. Among extant imputation techniques, kNN imputation algorithm is the best one as it is a model free and efficient compared with other methods. However, the value of k must be chosen properly in using kNN imputation. In particular, when some nearest neighbors are far from a missing data, the kNN imputation algorithms are ...

A basic problem of proteomics is identifying the subcellular locations of a protein. One factor making the problem more complicated is that some proteins may simultaneously exist in two or more than two subcellular locations. To improve multisite prediction quality, it is necessary to use effective feature extraction methods. Here, we developed a new feature extraction method based on the pK va...

A distance metric learned from data reflects the actual similarity between objects better than the geometric distance. So, in this paper, we propose a new distance that is based on clustering. Because objects belonging to the same cluster usually share some common traits even though their geometric distance might be large. Thus, we perform several clustering runs to yield an ensemble of cluster...

This paper presents an algorithm to identify a person through his emotional state using neural network which are completed in two phase, first is database creation in which we take user image and an audio file as an input, and add the features to the database. This is done by the code named createDatabase.m in MATLAB. Second is evaluation of Database where the system evaluates the parameters an...

In Sentiment analysis we use natural language processing and information to extracting writer’s comments or reviews. In this paper we use Data text mining and hybrid approach of KNN Algorithm and Naïve Bayes Algorithm to find the sentiments of Indian people on Tweeter.

We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. R...