Based on data types, four learning methods have been presented to extract patterns from
data: supervised, semi-supervised, unsupervised, and reinforcement. Regarding machine …

In this article, we consider the semi-supervised data stream classification problems. Most of
the semi-supervised learning algorithms suffer from a proper selection metric to select from …

The main focus of this article is to identify relationships among prognostic indices for
different breast cancer groups, using classification algorithms in the field of data mining …

Inthis article, wefocus on the classification problem to semi-supervised learning in non-
stationary environment. Semi-supervised learning is a learning task from both labeled and …

In this article we focus on the semi-supervised learning. Semi-supervised learning typically
is a learning task from both labeled and unlabeled data. We especially consider the …

In recent years, deep learning methods have been developed in order to solve the
problems. These methods were effective in solving complex problems. Convolution is one of …

We present an intelligent sample selection approach to language model adaptation for
handwritten text recognition, which exploits a combination of in-domain and out-of-domain …

Systems, methods, and computer-readable media for build ing ensemble members of a
Support Vector Machine (SVM) ensemble in parallel and executing processing in parallel on …

Skin disease cases are becoming more common, and diagnosing these diseases in a clinic
is never an easy task. A deep learning (DL) based model was previously used to diagnose …

Nowadays cyber and telecommunication criminal activities are becoming more
sophisticated and hazardous. Often, adversaries form large teams composed of hundreds of …