In this paper, a new topic identification method, WSIM, is investigated. It exploits the similarity between words and topics. This measure is a function of the similarity between words, based on the mutual information. The performance of WSIM is compared to the cache model and to the wellknown SVM classifier. Their behavior is also studied in terms of recall and precision, according to the trai...

The development of biosensors that produce time series data will facilitate improvements in biomedical diagnostics and in personalized medicine. The time series produced by these devices often contains characteristic features arising from biochemical interactions between the sample and the sensor. To use such characteristic features for determining sample class, similarity-based classifiers can...

Perhaps the most straightforward classifier in arsenal or Machine Learning techniques is Nearest Neighbour Classifier—classification achieved by identifying nearest neighbours to a query example and using those determine class of query. This approach classification particular importance, because issues poor runtime performance not such problem these days with computational power that available....

By imitating the way that heat flows in a medium with a geometric structure, we propose two novel classification algorithms, Non-propagating Heat Diffusion Classifier (NHDC) and Propagating Heat Diffusion Classifier (PHDC). In NHDC, an unlabelled data is classified into the class that diffuses the most heat to the unlabelled data after one local diffusion from time 0 to a small time period, whi...

Objectives: Designing optical character recognition systems for Kannada is challenging due to higher self-similarity in characters and number of classes. This work addresses the two major problems reduced accuracy false positives characters. Methods: proposes a stage multi modal deep learning technique handle complexity recognition. The are first grouped based on morphological structural simila...

This paper addresses similarity model and term association for similarity-based document categorization. Both Euclidean distance– and cosine-based similarity models are widely used for measures of document similarity in information retrieval and document categorization community. These two similarity models are based on the assumption that term vectors are orthogonal. Term associations are igno...

In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In this paper, we construct a complete graph-based binary classifier given only samples’ feature vectors and partial labels. Specifically, we first build appropriate similarity graphs with positive and negative edge weight...

in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...