S
Santanu Ghorai
Researcher at Heritage Institute of Technology
Publications - 30
Citations - 675
Santanu Ghorai is an academic researcher from Heritage Institute of Technology. The author has contributed to research in topics: Support vector machine & Kernel (statistics). The author has an hindex of 11, co-authored 28 publications receiving 561 citations. Previous affiliations of Santanu Ghorai include MCKV Institute of Engineering & Indian Institute of Technology Kharagpur.
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Automatic Defect Detection on Hot-Rolled Flat Steel Products
TL;DR: Test results reveal that three-level Haar feature set is more promising to address the problem of automatic defect detection on hot-rolled steel surface than the other wavelet feature sets as well as texture-based segmentation or thresholding technique of defect detection.
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Nonparallel plane proximal classifier
TL;DR: The formulation of NPPC for binary data classification is based on two identical mean square error (MSE) optimization problems which lead to solving two small systems of linear equations in input space and it eliminates the need of any specialized software for solving the quadratic programming problems (QPPs).
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Cancer Classification from Gene Expression Data by NPPC Ensemble
TL;DR: A nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a computer-aided diagnosis (CAD) framework than that of a single NPPC model is presented.
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Newton's method for nonparallel plane proximal classifier with unity norm hyperplanes
TL;DR: The reformulated NPPC is reformulated by considering equality constraints and solved by Newton's method and the solution is updated by solving a system of linear equations by conjugate gradient method, indicating enhanced computational efficiency of nonlinear NPPC on large data sets with the proposed NPPC framework.
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Discriminant Analysis for Fast Multiclass Data Classification Through Regularized Kernel Function Approximation
TL;DR: The effectiveness of the proposed VVRKFA method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification.