Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Since about 100 years ago, to learn the intrinsic structure of data, many representation
learning approaches have been proposed, either linear or nonlinear, either supervised or …

In this paper, a new multi-class classification framework incorporating geometric data
relationships described in both intrinsic and penalty graphs in multi-class Support Vector …

A common way of solving a multi-class classification problem is to decompose it into a
collection of simpler two-class problems. One major disadvantage is that with such a binary …

Most learning-based hashing algorithms leverage sample-to-sample similarities, such as
neighborhood structure, to generate binary codes, which achieve promising results for …

Participatory sensing (PS) is an emerging socio-technological paradigm in which citizens
voluntarily participate and contribute to a distributed information system using applications …

In recent years, deep neural network is widely used in machine learning. The multi-class
classification problem is a class of important problem in machine learning. However, in order …

Accurate classification and recognition of pulmonary nodules is an important and key
process of Computer-Aided Diagnosis (CAD) system in lung cancer diagnose. Although it …

Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification,
which is a core problem in Pattern Recognition and Machine Learning. A major advantage …

Multiclass classification using Support Vector Machine (SVM) is an ongoing research issue.
SVM is mainly a binary classifier, but for classification efficiency, it is also used for multiclass …