What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Machine learning
His main research concerns Support vector machine, Linear programming, Artificial intelligence, Mathematical optimization and Nonlinear system.
His Support vector machine research includes themes of Theoretical computer science, Kernel, Minification, Algorithm and Test set.
His Linear programming study incorporates themes from Applied mathematics, System of linear equations, Surgical biopsy and Feature vector.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Breast tumor and Pattern recognition.
His research investigates the connection between Mathematical optimization and topics such as Nonlinear programming that intersect with problems in Farkas' lemma, Sequential quadratic programming and Invex function.
In his research on the topic of Nonlinear system, Monotone polygon and Smoothing is strongly related with Mathematical analysis.
His most cited work include:
- Nonlinear Programming (1986 citations)
- Feature Selection via Concave Minimization and Support Vector Machines (859 citations)
- Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology (772 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Linear programming, Mathematical optimization, Support vector machine, Artificial intelligence and Applied mathematics.
The study incorporates disciplines such as Discrete mathematics, Square matrix and Linear inequality in addition to Linear programming.
His Quadratic programming study in the realm of Mathematical optimization interacts with subjects such as Complementarity theory, Mixed complementarity problem and Linear complementarity problem.
His biological study spans a wide range of topics, including Classifier, Test set and Nonlinear system.
His research integrates issues of Machine learning, Data mining and Pattern recognition in his study of Artificial intelligence.
His research on Applied mathematics also deals with topics like
- Mathematical analysis and related Absolute value equation and Minification,
- Combinatorics, which have a strong connection to Point.
He most often published in these fields:
- Linear programming (42.92%)
- Mathematical optimization (29.20%)
- Support vector machine (20.35%)
What were the highlights of his more recent work (between 2004-2017)?
- Linear programming (42.92%)
- Support vector machine (20.35%)
- Absolute value equation (12.39%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Linear programming, Support vector machine, Absolute value equation, Artificial intelligence and Discrete mathematics.
The subject of his Linear programming research is within the realm of Mathematical optimization.
His studies deal with areas such as Classifier, Regular polygon, Simple, Algorithm and Breast cancer as well as Support vector machine.
His Absolute value equation research also works with subjects such as
- Square matrix, which have a strong connection to Combinatorics and Bilinear interpolation,
- Mathematical analysis that intertwine with fields like Applied mathematics and Minification.
Olvi L. Mangasarian has researched Artificial intelligence in several fields, including Machine learning, Nonlinear system, Data mining and Pattern recognition.
His work deals with themes such as Cutting stock problem, Change-making problem, Continuous knapsack problem, Linear equation and Finite set, which intersect with Discrete mathematics.
Between 2004 and 2017, his most popular works were:
- Multisurface proximal support vector machine classification via generalized eigenvalues (563 citations)
- Multicategory Proximal Support Vector Machine Classifiers (252 citations)
- Absolute value equations (156 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Real number
- Machine learning
Olvi L. Mangasarian spends much of his time researching Linear programming, Mathematical optimization, Support vector machine, Mathematical analysis and Absolute value equation.
His research on Linear programming focuses in particular on Linear-fractional programming.
His studies in Mathematical optimization integrate themes in fields like Differentiable function, Norm, Newton's method and Regular polygon.
His Support vector machine study results in a more complete grasp of Artificial intelligence.
Olvi L. Mangasarian combines subjects such as Nonlinear system and Pattern recognition with his study of Artificial intelligence.
His work on Absolute value as part of general Mathematical analysis research is often related to Linear complementarity problem, thus linking different fields of science.
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.
