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Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
LSU RESEARCH magazine informs readers about university research programs. Apollo's Lyre is a poetry and fiction magazine published each semester by the Honors College. LSU Alumni Magazine is a quarterly which focuses on Alumni success and current university news sent out to alumni everywhere. Gumbo is the university's yearbook, which may be ...
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. [1] SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.
Louisiana State University Shreveport (LSU Shreveport or LSUS) is a public university in Shreveport, Louisiana. It is part of the Louisiana State University System . Initially, a two-year college, LSUS has expanded into a university with 25 undergraduate degree programs, 13 master's degree programs, and a Doctorate of Education in Leadership ...
Louisiana State University of Alexandria (LSU of Alexandria or LSUA, formerly Louisiana State University at Alexandria [3]) is a public college in Alexandria, Louisiana. It offers undergraduate degrees in numerous disciplines. The university is a unit of the LSU System and operates under the auspices of the Louisiana Board of Regents. [4]
LSU football fell short in tough road matchup loss to No. 11 Texas A&M in Week 9.. The No. 16 Tigers (6-2, 3-1 SEC) are one loss away from likely elimination from contention in the 12-team College ...
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels. As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree .