R
Rajasekar Venkatesan
Researcher at Nanyang Technological University
Publications - 19
Citations - 681
Rajasekar Venkatesan is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Classifier (UML) & Multiclass classification. The author has an hindex of 7, co-authored 19 publications receiving 460 citations.
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graph2vec: Learning Distributed Representations of Graphs
Annamalai Narayanan,Mahinthan Chandramohan,Rajasekar Venkatesan,Lihui Chen,Yang Liu,Shantanu Jaiswal +5 more
TL;DR: This work proposes a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs that achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.
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Sentiment classification using Comprehensive Attention Recurrent models
TL;DR: A new architecture termed Comprehensive Attention Recurrent Neural Networks (CA-RNN) which can store preceding, succeeding and local contexts of any position in a sequence is developed and can achieve competitive performance compared with the state-of-the-art approaches.
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A novel online multi-label classifier for high-speed streaming data applications
TL;DR: Experimental studies shows that the proposed technique outperforms the existing multi-label classifiers in terms of performance and speed.
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A novel online multi-label classifier for high-speed streaming data applications
TL;DR: In this article, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed, where each of the input data sample belongs to one or more than one of the target labels.
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A novel progressive learning technique for multi-class classification
Rajasekar Venkatesan,Meng Joo Er +1 more
TL;DR: A progressive learning technique for multi-class classification is proposed that can learn new classes while still retaining the knowledge of previous classes and is suitable for real-world applications where the number of classes is often unknown and online learning from real-time data is required.