scholarly journals Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 62458-62470
Author(s):  
Peipei Wang ◽  
Xinqi Zheng ◽  
Junhua Ku ◽  
Chunning Wang
Keyword(s):  
Extreme Learning Machine ◽  
Multiple Instance Learning ◽  
Learning Approach ◽  
Learning Machine

scholarly journals Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Chengzhang Zhu ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
Yuewei Ming ◽  
Jianping Yin
Keyword(s):  
Extreme Learning Machine ◽  
High Efficiency ◽  
Computational Cost ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Normal Vector ◽  
Learning Approach ◽  
Multiple Kernel ◽  
Learning Machine

We propose a distance based multiple kernel extreme learning machine (DBMK-ELM), which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, anl2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM). Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.

Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

2017 ◽  
Vol 26 (1) ◽  
pp. 185-195 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Xin Zhao
Keyword(s):  
Extreme Learning Machine ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Data Sets ◽  
Rbf Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

State Preserving Extreme Learning Machine: A Monotonically Increasing Learning Approach

2016 ◽  
Vol 45 (2) ◽  
pp. 703-725 ◽  
Author(s):  
Md. Zahangir Alom ◽  
Paheding Sidike ◽  
Tarek M. Taha ◽  
Vijayan K. Asari
Keyword(s):  
Extreme Learning Machine ◽  
Learning Approach ◽  
Learning Machine

scholarly journals A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Jinzhu Peng ◽  
Yuheng Jia
Keyword(s):  
Extreme Learning Machine ◽  
Multiple Instance Learning ◽  
Data Sets ◽  
Feedforward Network ◽  
Real World Data ◽  
Benchmark Data ◽  
Input And Output ◽  
Learning Machine ◽  
Hidden Layer

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.

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