What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Machine learning
- Artificial intelligence
Suresh Chandra mainly investigates Mathematical optimization, Artificial intelligence, Fuzzy set operations, Fuzzy number and Fuzzy classification.
His primary area of study in Mathematical optimization is in the field of Duality.
His Duality study integrates concerns from other disciplines, such as Maximization, Fuzzy programming, Fractional programming, Convexity and Algebra.
Much of his study explores Artificial intelligence relationship to Constrained optimization.
His Support vector machine research includes elements of Quadratic programming and Regression.
His Quadratic programming research is multidisciplinary, incorporating perspectives in Binary number, Ranking SVM, Kernel, Generalization and Pattern recognition.
His most cited work include:
- Twin Support Vector Machines for Pattern Classification (1145 citations)
- Fuzzy Mathematical Programming and Fuzzy Matrix Games (179 citations)
- Matrix games with fuzzy goals and fuzzy payoffs (116 citations)
What are the main themes of his work throughout his whole career to date?
Suresh Chandra spends much of his time researching Mathematical optimization, Support vector machine, Artificial intelligence, Duality and Fractional programming.
His Mathematical optimization research includes themes of Fuzzy number, Defuzzification, Applied mathematics, Fuzzy logic and Fuzzy set operations.
His biological study spans a wide range of topics, including Fuzzy mathematics and Fuzzy associative matrix.
His work carried out in the field of Support vector machine brings together such families of science as Classifier, Algorithm, Quadratic programming and Benchmark.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Hyperplane.
The Duality study combines topics in areas such as Convexity, Minimax and Algebra.
He most often published in these fields:
- Mathematical optimization (42.11%)
- Support vector machine (30.92%)
- Artificial intelligence (19.74%)
What were the highlights of his more recent work (between 2015-2021)?
- Support vector machine (30.92%)
- Artificial intelligence (19.74%)
- Quadratic programming (10.53%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Support vector machine, Artificial intelligence, Quadratic programming, Function and Hyperplane.
His research in Support vector machine intersects with topics in Training set, Classifier, Applied mathematics, Benchmark and Algorithm.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His studies deal with areas such as Microeconomics, Stochastic dominance, Risk aversion and Trading strategy as well as Quadratic programming.
His research investigates the connection with Hyperplane and areas like Parametric statistics which intersect with concerns in Hinge loss, Classifier and Margin.
In his research on the topic of Linear programming, Mathematical optimization is strongly related with Simple.
Between 2015 and 2021, his most popular works were:
- TWSVR: Regression via Twin Support Vector Machine. (41 citations)
- Improvements on v -Twin Support Vector Machine (22 citations)
- Fuzzy least squares twin support vector clustering (21 citations)
In his most recent research, the most cited papers focused on:
- Machine learning
- Artificial intelligence
- Mathematical optimization
Support vector machine, Artificial intelligence, Hyperplane, Structured support vector machine and Quadratic programming are his primary areas of study.
His work on Benchmark expands to the thematically related Support vector machine.
His Artificial intelligence research focuses on Pattern recognition and how it relates to System of linear equations.
His Hyperplane study combines topics from a wide range of disciplines, such as Mathematical optimization, Simple and Parametric statistics.
His Structured support vector machine research integrates issues from Optimization problem and Relevance vector machine.
Suresh Chandra combines subjects such as Regularization, Line search, Binary classification and Generalization, Structural risk minimization with his study of Quadratic programming.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
