Neural Network Approach for Multiple-Class Classification

Abstract

Classification is a very important problem-solving technique. Given a set of attribute values about an object, a classifier determines which category the object belongs to. A classification task can be a binary or multiple classification; it can also be a single-label or multiple-label classification. Neural networks are an effective technique in the field of artificial intelligence. In this paper, we develop different schemes of neural networks for solving binary, multiple, single-label, and multiple-label classification problems. The ideas behind them are described in detail.