Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review
Support vector machines (SVMs) are widely used classifiers for detecting physiological
patterns in human–computer interaction (HCI). Their success is due to their versatility …
patterns in human–computer interaction (HCI). Their success is due to their versatility …
Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods
Binary financial distress prediction (FDP), which categorizes corporate financial status into
the two classes of distress and nondistress, cannot provide enough support for effective …
the two classes of distress and nondistress, cannot provide enough support for effective …
On the appropriateness of Platt scaling in classifier calibration
B Böken - Information Systems, 2021 - Elsevier
Many applications using data mining and machine learning techniques require posterior
probability estimates besides often highly accurate predictions. Classifier calibration is a …
probability estimates besides often highly accurate predictions. Classifier calibration is a …
DRCW-OVO: distance-based relative competence weighting combination for one-vs-one strategy in multi-class problems
One-vs-One strategy is a common and established technique in Machine Learning to deal
with multi-class classification problems. It consists of dividing the original multi-class …
with multi-class classification problems. It consists of dividing the original multi-class …
Traffic sign recognition using group sparse coding
Recognizing traffic signs is a challenging problem; and it has captured the attention of the
computer vision community for several decades. Essentially, traffic sign recognition is a multi …
computer vision community for several decades. Essentially, traffic sign recognition is a multi …
A genetically optimized neural network model for multi-class classification
Multi-class classification is one of the major challenges in real world application.
Classification algorithms are generally binary in nature and must be extended for multi-class …
Classification algorithms are generally binary in nature and must be extended for multi-class …
Switching topology approach for UAV formation based on binary-tree network
D Zhang, H Duan - Journal of the Franklin Institute, 2019 - Elsevier
Formation control is one of the most active topics within the realm of coordination fields for
unmanned aerial vehicles (UAVs). The formation pattern is an essential aspect which mainly …
unmanned aerial vehicles (UAVs). The formation pattern is an essential aspect which mainly …
An adaptive strategy selection method with reinforcement learning for robotic soccer games
H Shi, Z Lin, KS Hwang, S Yang, J Chen - IEEE Access, 2018 - ieeexplore.ieee.org
Robotic soccer games, which have become popular, require timely and precise
decisionmaking in a dynamic environment. To address the problems of complexity in a …
decisionmaking in a dynamic environment. To address the problems of complexity in a …
Posterior probability based ensemble strategy using optimizing decision directed acyclic graph for multi-class classification
L Zhou, H Fujita - Information Sciences, 2017 - Elsevier
Ensemble strategy is important to develop a decomposition and ensemble method for multi-
class classification problems. Most existing ensemble strategies use predetermined and …
class classification problems. Most existing ensemble strategies use predetermined and …
Empowering difficult classes with a similarity-based aggregation in multi-class classification problems
One-vs-One strategy divides the original multi-class problem into as many binary
classification problems as pairs of classes. Then, independent base classifiers are learned …
classification problems as pairs of classes. Then, independent base classifiers are learned …