Regularized Weighted Circular Complex-Valued Extreme Learning Machine for Imbalanced Learning
نویسندگان
چکیده
منابع مشابه
Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems
Article history: Received 9 November 2010 Received in revised form 14 September 2011 Accepted 6 November 2011 Available online 13 November 2011
متن کاملA Robust and Regularized Extreme Learning Machine
In a moment when the study of outlier robustness within Extreme Learning Machine is still in its infancy, we propose a method that combines maximization of the hidden layer’s information transmission, through Batch Intrinsic Plasticity (BIP), with robust estimation of the output weights. This method named R-ELM/BIP generates a reliable solution in the presence of corrupted data with a good gene...
متن کاملA Novel Neutrosophic Weighted Extreme Learning Machine for Imbalanced Data Set
Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as good generalization performance, fast learning speed, and low computational cost. However, the ELM might have problems in the classification of im...
متن کاملFully complex extreme learning machine
Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, ...
متن کاملRegularized Extreme Learning Machine for Large-scale Media Content Analysis
In this paper, we propose a new regularization approach for Extreme Learning Machine-based Singlehidden Layer Feedforward Neural network training. We show that the proposed regularizer is able to weight the dimensions of the ELM space according to the importance of the network’s hidden layer weights, without imposing additional computational and memory costs in the network learning process. Thi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2015
ISSN: 2169-3536
DOI: 10.1109/access.2015.2506601