Target encoding plays a central role when learning Convolutional Neural Networks. In this
realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so …

AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis
and has made great success in practical applications. AdaBoost can boost a weak learning …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

摘要AdaBoost 是最优秀的Boosting 算法之一, 有着坚实的理论基础, 在实践中得到了很好的
推广和应用. 算法能够将比随机猜测略好的弱分类器提升为分类精度高的**分类器 …

Multiclass classification is a fundamental and challenging task in machine learning. The
existing techniques of multiclass classification can be categorized as (1) decomposition into …

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm
has been widely used because of its outstanding performance in co** with highly non …

The difficulty of the cancer classification using multi-class microarray datasets lies in that
there are only a few samples in each class. To effectively solve such a problem, we propose …

The paper presents a new methodology within the framework of the so-called compliant
class-models, PLS2-CM, designed with the purpose of improving the performance of class …

Abstract Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass
classification problem by reducing the original multi-class problem to several binary sub …

Given the growing urge for plastic management and regulation in the world, recent studies
have investigated the problem of plastic material identification for correct classification and …