Support Vector Machines (SVM) have become a widespread method in machine learning applications. In this paper we studied the one-class SVM method, whose goal is to describe the data from a single class by a set of support vectors. One-class SVMs can be used to construct multiple classifier systems (MCS) utilising the individual one-class data descriptions together with strategies to combine the...

The basic task in intrusion detection system is to classify network activities as normal or abnormal while minimizing misclassification. In literature, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) to protect the special computer systems, vulnerable traffics cyber-attacks for computer networks. In addition, Support Vector Machine (SV...

Low level features like color etc. of an image are really very important for any image retrieval system. This paper implements image classification technique using SURF descriptor and SVM classifier. SURF method which is advanced version of SIFT is used to match feature points of training and test images. SVM classifier based on the outcome of feature points then classifies images. Through the ...

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and...

Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems i...

We propose a privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns as well as individual data point rows are divided into groups belonging to different entities. Each entity is unwilling to make public its group of columns and rows. Our classifier utilizes the entire data matrix A while maintaining the privacy of each block. This classifier i...

In this project a classifier is developed that can classify a blog into different categories based on the topics that author frequently writes about. Treating each blog as a document and each topic category as a class, a SVM based multi-class classifier is developed. The impact of feature selection has been studied by using different methods to generate the feature vector from the documents.