Multicategory discrimination via linear programming
نویسندگان
چکیده
منابع مشابه
Serial and Parallel Multicategory Discrimination
A parallel algorithm is proposed for a fundamental problem of machine learning, that of mul-ticategory discrimination. The algorithm is based on minimizing an error function associated with a set of highly structured linear inequalities. These inequalities characterize piecewise-linear separation of k sets by the maximum of k aane functions. The error function has a Lipschitz continuous gradien...
متن کاملFeature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming
We reformulate branch-and-bound feature selection employing L1 or particular Lp metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.
متن کاملLinear discrimination using second order conic programming
We propose a new p-norm linear discrimination model that generalizes the model of Bennett and Mangasarian (1992) and reduces to linear programming problem with p-order conic constraints. We demonstrate that the developed model possesses excellent methodological and computational properties (e.g., it does not allow for a null separating hyperplane when the sets are linearly separable, etc). The ...
متن کاملOblique Multicategory Decision Trees Using Nonlinear Programming
I of decision trees is a popular and effective method for solving classification problems in data-mining applications. This paper presents a new algorithm for multi-category decision tree induction based on nonlinear programming. This algorithm, termed OC-SEP (Oblique Category SEParation), combines the advantages of several other methods and shows improved generalization performance on a collec...
متن کاملPiecewise affine regression via recursive multiple least squares and multicategory discrimination
In nonlinear regression choosing an adequate model structure is often a challenging problem. While simple models (such as linear functions) may not be able to capture the underlying relationship among the variables, over-parametrized models described by a large set of nonlinear basis functions tend to overfit the training data, leading to poor generalization on unseen data. Piecewise-affine (PW...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Optimization Methods and Software
سال: 1994
ISSN: 1055-6788,1029-4937
DOI: 10.1080/10556789408805554