6 code implementations • 17 Jul 2017 • Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, Shantanu Jaiswal
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
Ranked #1 on Malware Detection on Android Malware Dataset
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
In this paper, a progressive learning technique for multi-class classification is proposed.
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 • 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 • 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 • 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.