J
Jiuwen Cao
Researcher at Hangzhou Dianzi University
Publications - 185
Citations - 4340
Jiuwen Cao is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Extreme learning machine & Computer science. The author has an hindex of 29, co-authored 151 publications receiving 3029 citations. Previous affiliations of Jiuwen Cao include University of Electronic Science and Technology of China & Nanyang Technological University.
Papers
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Journal ArticleDOI
Voting based extreme learning machine
TL;DR: The proposed method incorporates the voting method into the popular extreme learning machine (ELM) in classification applications and generally outperforms the original ELM algorithm as well as several recent classification algorithms.
Journal ArticleDOI
Self-Adaptive Evolutionary Extreme Learning Machine
TL;DR: Simulations have shown that SaE-ELM not only performs better than E- ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods.
Journal ArticleDOI
Extreme learning machine and adaptive sparse representation for image classification
TL;DR: Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.
Proceedings ArticleDOI
Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation
TL;DR: This paper proposes a novel deep neural network architecture based on transfer learning for microscopic image classification that produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods.
Journal ArticleDOI
Kernel-Based Multilayer Extreme Learning Machines for Representation Learning
TL;DR: Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are elimination of manual tuning on the number of hidden nodes in every layer and no random projection mechanism so as to obtain optimal model generalization.