What is he best known for?
The fields of study he is best known for:
- Machine learning
- Artificial intelligence
- Gene
Chih-Jen Lin mainly focuses on Support vector machine, Artificial intelligence, Machine learning, Mathematical optimization and Structured support vector machine.
His work on Multiclass classification and Sequential minimal optimization as part of general Support vector machine research is frequently linked to Working set, bridging the gap between disciplines.
His work carried out in the field of Sequential minimal optimization brings together such families of science as Radial basis function kernel, Graph kernel and Hinge loss.
Chih-Jen Lin has included themes like Sparse matrix and Pattern recognition in his Artificial intelligence study.
While the research belongs to areas of Machine learning, he spends his time largely on the problem of Data mining, intersecting his research to questions surrounding Probabilistic forecasting.
His Structured support vector machine study combines topics in areas such as Optimization problem and Relevance vector machine.
His most cited work include:
- LIBSVM: A library for support vector machines (35046 citations)
- LIBLINEAR: A Library for Large Linear Classification (5734 citations)
- A comparison of methods for multiclass support vector machines (5364 citations)
What are the main themes of his work throughout his whole career to date?
Chih-Jen Lin mainly investigates Artificial intelligence, Support vector machine, Machine learning, Mathematical optimization and Algorithm.
The Linear classifier research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Scale, therefore creating a link between diverse domains of science.
His study in the field of Relevance vector machine, Structured support vector machine and Sequential minimal optimization also crosses realms of Decomposition.
Machine learning is closely attributed to Data mining in his research.
Chih-Jen Lin has researched Mathematical optimization in several fields, including Principle of maximum entropy and Applied mathematics.
Within one scientific family, he focuses on topics pertaining to Newton's method under Algorithm, and may sometimes address concerns connected to Trust region.
He most often published in these fields:
- Artificial intelligence (36.17%)
- Support vector machine (35.11%)
- Machine learning (24.47%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (36.17%)
- Algorithm (15.43%)
- Optimization problem (7.98%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Algorithm, Optimization problem, Linear classifier and Newton's method.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence.
His Machine learning study which covers Data mining that intersects with Factorization.
His Linear classifier study incorporates themes from Regularization, Applied mathematics, Overfitting and Conjugate gradient method.
The various areas that Chih-Jen Lin examines in his Binary classification study include Binary number, Multiclass classification and Relevance vector machine.
Particularly relevant to Structured support vector machine is his body of work in Support vector machine.
Between 2014 and 2021, his most popular works were:
- A Comparison of Methods for Multi-class Support Vector Machines (381 citations)
- Field-aware Factorization Machines for CTR Prediction (293 citations)
- A LINE1-Nucleolin Partnership Regulates Early Development and ESC Identity. (169 citations)
In his most recent research, the most cited papers focused on:
- Machine learning
- Gene
- Statistics
His primary areas of investigation include Recommender system, Artificial intelligence, Matrix decomposition, Selection and Machine learning.
His study brings together the fields of Pattern recognition and Artificial intelligence.
His studies in Selection integrate themes in fields like Incomplete Cholesky factorization, Optimization problem, Mathematical optimization and Theoretical computer science.
When carried out as part of a general Machine learning research project, his work on Binary classification is frequently linked to work in Implementation, therefore connecting diverse disciplines of study.
Chih-Jen Lin interconnects Multiclass classification and Selection bias in the investigation of issues within Binary classification.
His biological study spans a wide range of topics, including Structured support vector machine, Support vector machine, Relevance vector machine and Binary number.
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