Journal Article10.1016/J.NEUCOM.2017.04.052
A multi-label classification algorithm based on kernel extreme learning machine
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TL;DR: K Kernel extreme learning machine was applied to multi-label classification problem (ML-KELM) in this paper, so the iterative learning operations can be avoided and a dynamic, self-adaptive threshold function was designed to solve the transformation from ML-K ELM network’s real-value outputs to binary multi- label vector.
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About: This article is published in Neurocomputing. The article was published on 18 Oct 2017. The article focuses on the topics: Multi-label classification & Extreme learning machine.
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References
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
11.6K
Extreme Learning Machine for Regression and Multiclass Classification
Guang-Bin Huang,Hongming Zhou,Xiaojian Ding,Rui Zhang +3 more
- 01 Apr 2012
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
5.7K
ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi- label learning algorithms.
3.6K
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
3.4K
RCV1: A New Benchmark Collection for Text Categorization Research
TL;DR: This work describes the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data.