Integer Optimization Techniques - S. Shenbaga Ezhil,B. K. Jaleesha,S. Rajababu
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There is a variety of method for solving classification problem in different disciplines. Some of these methods include Neural Networks (NN), fuzzy logic, support vector machines (SVM), principal component analysis P(A), tolerant rough sets, linear programming.Finally, we would like to expand the applications of our methodologies. For example, we can extend the regression problem to those with linear constr ... Full description
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Description
There is a variety of method for solving classification problem in different disciplines. Some of these methods include Neural Networks (NN), fuzzy logic, support vector machines (SVM), principal component analysis P(A), tolerant rough sets, linear programming.Finally, we would like to expand the applications of our methodologies. For example, we can extend the regression problem to those with linear constraints. There may be bounds on the value of the regression coefficients, and limitations on the changes in the regression coefficients in time-series regression . We would be able to use our general methodology to solve this combined subset selection and constrained regression problem. Also, we can clearly extend our methodologies to general quadratic mixed-integer optimization as well.
More Information
| Author | S. Shenbaga Ezhil, B. K. Jaleesha, S. Rajababu |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2020 |
| Cover type | Softcover |
| EAN | 9786202529860 |