2 research outputs found
Single Versus Union: Non-parallel Support Vector Machine Frameworks
Considering the classification problem, we summarize the nonparallel support
vector machines with the nonparallel hyperplanes to two types of frameworks.
The first type constructs the hyperplanes separately. It solves a series of
small optimization problems to obtain a series of hyperplanes, but is hard to
measure the loss of each sample. The other type constructs all the hyperplanes
simultaneously, and it solves one big optimization problem with the ascertained
loss of each sample. We give the characteristics of each framework and compare
them carefully. In addition, based on the second framework, we construct a
max-min distance-based nonparallel support vector machine for multiclass
classification problem, called NSVM. It constructs hyperplanes with large
distance margin by solving an optimization problem. Experimental results on
benchmark data sets and human face databases show the advantages of our NSVM
An enhanced KNN-based twin support vector machine with stable learning rules
Among the extensions of twin support vector machine (TSVM), some scholars
have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification
accuracy. However, these KNN-based TSVM classifiers have two major issues such
as high computational cost and overfitting. In order to address these issues,
this paper presents an enhanced regularized K-nearest neighbor based twin
support vector machine (RKNN-TSVM). It has three additional advantages: (1)
Weight is given to each sample by considering the distance from its nearest
neighbors. This further reduces the effect of noise and outliers on the output
model. (2) An extra stabilizer term was added to each objective function. As a
result, the learning rules of the proposed method are stable. (3) To reduce the
computational cost of finding KNNs for all the samples, location difference of
multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded
into the learning process of the proposed method. The extensive experimental
results on several synthetic and benchmark datasets show the effectiveness of
our proposed RKNN-TSVM in both classification accuracy and computational time.
Moreover, the largest speedup in the proposed method reaches to 14 times.Comment: This paper was written in the summer of 2018. It is a part of Mir's
MSc thesi