A Fuzzy Least Squares Support Tensor Machines in Machine Learning
نویسندگان
چکیده
منابع مشابه
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two nonparallel hyperplanes are found by solving two systems of linear equations. Although, the algorithm is very fast and efficient in many classification tasks, it is unable to cope with tw...
متن کاملLeast Squares Support Vector Machine for Constitutive Modeling of Clay
Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...
متن کاملFuzzy least squares support vector machines for multiclass problems
In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...
متن کاملLeast Squares One-class Support Vector Machine on Fuzzy Set
In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class...
متن کاملActive Learning for Sparse Least Squares Support Vector Machines
For least squares support vector machine (LSSVM) the lack of sparse, while the standard sparse algorithm exist a problem that it need to mark all of training data. We propose an active learning algorithm based on LSSVM to solve sparse problem. This method first construct a minimum classification LSSVM, and then calculate the uncertainty of the sample, select the closest category to mark the sam...
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: International Journal of Emerging Technologies in Learning (iJET)
سال: 2015
ISSN: 1863-0383
DOI: 10.3991/ijet.v10i8.5203