3 research outputs found
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
Stochastic gradient descent algorithm has been successfully applied on
support vector machines (called PEGASOS) for many classification problems. In
this paper, stochastic gradient descent algorithm is investigated to twin
support vector machines for classification. Compared with PEGASOS, the proposed
stochastic gradient twin support vector machines (SGTSVM) is insensitive on
stochastic sampling for stochastic gradient descent algorithm. In theory, we
prove the convergence of SGTSVM instead of almost sure convergence of PEGASOS.
For uniformly sampling, the approximation between SGTSVM and twin support
vector machines is also given, while PEGASOS only has an opportunity to obtain
an approximation of support vector machines. In addition, the nonlinear SGTSVM
is derived directly from its linear case. Experimental results on both
artificial datasets and large scale problems show the stable performance of
SGTSVM with a fast learning speed.Comment: 31 pages, 31 figure
NLS: an accurate and yet easy-to-interpret regression method
An important feature of successful supervised machine learning applications
is to be able to explain the predictions given by the regression or
classification model being used. However, most state-of-the-art models that
have good predictive power lead to predictions that are hard to interpret.
Thus, several model-agnostic interpreters have been developed recently as a way
of explaining black-box classifiers. In practice, using these methods is a slow
process because a novel fitting is required for each new testing instance, and
several non-trivial choices must be made. We develop NLS (neural local
smoother), a method that is complex enough to give good predictions, and yet
gives solutions that are easy to be interpreted without the need of using a
separate interpreter. The key idea is to use a neural network that imposes a
local linear shape to the output layer. We show that NLS leads to predictive
power that is comparable to state-of-the-art machine learning models, and yet
is easier to interpret
A general model for plane-based clustering with loss function
In this paper, we propose a general model for plane-based clustering. The
general model contains many existing plane-based clustering methods, e.g.,
k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector
clustering (TWSVC) and its extensions. Under this general model, one may obtain
an appropriate clustering method for specific purpose. The general model is a
procedure corresponding to an optimization problem, where the optimization
problem minimizes the total loss of the samples. Thereinto, the loss of a
sample derives from both within-cluster and between-cluster. In theory, the
termination conditions are discussed, and we prove that the general model
terminates in a finite number of steps at a local or weak local optimal point.
Furthermore, based on this general model, we propose a plane-based clustering
method by introducing a new loss function to capture the data distribution
precisely. Experimental results on artificial and public available datasets
verify the effectiveness of the proposed method.Comment: 13 pages, 43 figure