In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.
Vladimir Naumovich Vapnik is a computer scientist, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms.
The UCPH Department of Computer Science is a department in the Faculty of Science at the University of Copenhagen (UCPH). It is the longest established department of Computer Science in Denmark and was founded in 1970 by Turing Award winner Peter Naur. As of 2021, it employs 82 academic staff, 126 research staff and 38 support staff. It is consistently ranked the top Computer Science department in the Nordic countries, and in 2017 was placed 9th worldwide by the Academic Ranking of World Universities.
In computer science, computational learning theory is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
Bernhard Schölkopf is a German computer scientist known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is also an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin, and chairman of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Paris Christos Kanellakis was a Greek American computer scientist.
ACM SIGACT or SIGACT is the Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory, whose purpose is support of research in theoretical computer science. It was founded in 1968 by Patrick C. Fischer.
Piotr Indyk is Thomas D. and Virginia W. Cabot Professor in the Theory of Computation Group at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
Eli Upfal is a computer science researcher, currently the Rush C. Hawkins Professor of Computer Science at Brown University. He completed his undergraduate studies in mathematics and statistics at the Hebrew University, Israel in 1978, received an M.Sc. in computer science from the Feinberg Graduate School of the Weizmann Institute of Science, Israel in 1980, and completed his PhD in computer science at the Hebrew University in 1983 under Eli Shamir. He has made contributions in a variety of areas. Most of his work involves randomized and/or online algorithms, stochastic processes, or the probabilistic analysis of deterministic algorithms. Particular applications include routing and communications networks, computational biology, and computational finance.
Cynthia Dwork is an American computer scientist best known for her contributions to cryptography, distributed computing, and algorithmic fairness. She is one of the inventors of differential privacy and proof-of-work.
Constantinos Daskalakis is a Greek theoretical computer scientist. He is a professor at MIT's Electrical Engineering and Computer Science department and a member of the MIT Computer Science and Artificial Intelligence Laboratory. He was awarded the Rolf Nevanlinna Prize and the Grace Murray Hopper Award in 2018.
Pavel Arkadevich Pevzner is the Ronald R. Taylor Professor of Computer Science and director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego. He serves on the editorial board of PLoS Computational Biology and he is a member of the Genome Institute of Singapore scientific advisory board.
Within mathematical analysis, Regularization perspectives on support-vector machines provide a way of interpreting support-vector machines (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids overfitting via Tikhonov regularization and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimator of the weights. Estimators with lower Mean squared error predict better or generalize better when given unseen data.
Katherine "Kathy" Anne Yelick, an American computer scientist, is the vice chancellor for research and the Robert S. Pepper Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She is also a faculty scientist at Lawrence Berkeley National Laboratory, where she was Associate Laboratory Director for Computing Sciences from 2010-2019.
Michael Justin Kearns is an American computer scientist, professor and National Center Chair at the University of Pennsylvania, the founding director of Penn's Singh Program in Networked & Social Systems Engineering (NETS), the founding director of Warren Center for Network and Data Sciences, and also holds secondary appointments in Penn's Wharton School and department of Economics. He is a leading researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational finance, algorithmic trading, computational social science and social networks. He previously led the Advisory and Research function in Morgan Stanley's Artificial Intelligence Center of Excellence team, and is currently an Amazon Scholar within Amazon Web Services.
Chih-Jen Lin is Distinguished Professor of Computer Science at National Taiwan University, and a leading researcher in machine learning, optimization, and data mining. He is best known for the open-source library LIBSVM, an implementation of support vector machines.
This page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included.
Maria-Florina (Nina) Balcan is a Romanian-American computer scientist whose research investigates machine learning, algorithmic game theory, theoretical computer science, including active learning, kernel methods, random-sampling mechanisms and envy-free pricing. She is an associate professor of computer science at Carnegie Mellon University.
Isabelle Guyon is a French-born researcher in machine learning known for her work on support-vector machines, artificial neural networks and bioinformatics. She is a Chair Professor at the University of Paris-Saclay.
Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.
