no code implementations • 1 Dec 2022 • Qiong Chang, Aolong Zha, Weimin WANG, Xin Liu, Masaki Onishi, Lei Lei, Meng Joo Er, Tsutomu Maruyama
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
no code implementations • 10 Sep 2020 • Liran Shen, Meng Joo Er, Qingbo Yin
The Classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields.
no code implementations • 21 Jun 2020 • Liran Shen, Meng Joo Er, Qingbo Yin
In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability.
no code implementations • 13 Nov 2017 • Yong Zhang, Hongming Zhou, Nganmeng Tan, Saeed Bagheri, Meng Joo Er
Audience interest, demography, purchase behavior and other possible classifications are ex- tremely important factors to be carefully studied in a targeting campaign.
no code implementations • 23 Sep 2016 • Mihika Dave, Sahil Tapiawala, Meng Joo Er, Rajasekar Venkatesan
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed.
no code implementations • 3 Sep 2016 • Meng Joo Er, Rajasekar Venkatesan, Ning Wang
Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification.
no code implementations • 1 Sep 2016 • Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama, Shiqian Wu
In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed.
no code implementations • 1 Sep 2016 • Rajasekar Venkatesan, Meng Joo Er
In this paper, a progressive learning technique for multi-class classification is proposed.
no code implementations • 31 Aug 2016 • Meng Joo Er, Rajasekar Venkatesan, Ning Wang
In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed.
no code implementations • 31 Aug 2016 • Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed.
no code implementations • 30 Aug 2016 • Rajasekar Venkatesan, Meng Joo Er
The comparative results shows that the proposed Extreme Learning Machine based multi-label classification technique is a better alternative than the existing state of the art methods for multi-label problems.