Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, ther...

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the ...

Learning from positive and unlabeled examples (PU learning) is a special case of semi-supervised binary classification. The key feature of PU learning is that there is no labeled negative training data, which makes the traditional classification techniques inapplicable. Similar to the idea of Biased-SVM which is one of the most famous classifier, a biased least squares support vector machine cl...

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsp...

Hybrid linear analysis (HLA), partial least-squares (PLS) regression, and the linear least square support vector machine (LSSVM) were used to determinate the soluble solids content (SSC) of apple by Fourier transform near-infrared (FT-NIR) spectroscopy. The performance of these three linear regression methods was compared. Results showed that HLA could be used for the analysis of complex solid ...

This paper presents a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database. The present study suggests a more robust technique that is likely to supply a value closer to the one that is missing for effective classification and diagnosis. Initially data is clustered a...

In the era of very rapidly advancing technology like today, both internet and computerization have made various corporate agencies or investors start thinking about importance stock market in their capital division. Previously there were purchases by company's capital, such: gold, land, buildings, production machines, but at this time purchase shares should also to attract attention these are l...

Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each pr...