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learning method
Linearization learning method of BP neural networks
      
This paper proposes a learning method linearizing non-linearity of the activation function and discusses its merits and demerits theoretically.
      
We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA+WV (Multi-Objective Evolutionary Algorithm+Weighted Voting).
      
Also, a new cooperative learning method called weighted strategy sharing (WSS) is introduced.
      
The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail.
      
The learning method leads to a linear programming problem and then: (a) the solution isobtained in a finite number of iterations, and (b) the global optimum is attained.
      
We also introduce a high capacity learning method that learns any permutably homogeneously separable k-valued function given as input.
      
In this paper, we re-derive the learning method from a probabilistic perspective and then show that similar networks can be derived based on the pioneering work of Becker [1] if certain simplifying assumptions are made.
      
The action-value function of the Q-learning method is approximated by the radial basis function neural network and learned by the gradient descent.
      
In this paper, we propose a new information theoretic competitive learning method.
      
We first construct a learning method in single-layered networks, and then we extend it to supervised multi-layered networks.
      
In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be approximated.
      
This letter presents a novel cooperative neural network ensemble learning method based on Negative Correlation learning.
      
Comparison with the best Negative Correlation learning method reported demonstrates comparable performance at significantly reduced communication overhead.
      
And support vector machine is a new machine learning method based on the statistical learning theory.
      
By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given.
      
Co-training is a semi-supervised learning method, which employs two complementary learners to label the unlabeled data for each other and to predict the test sample together.
      
The amnesic action of phencyclidine (PCP) was investigated in mice using a passive avoidance- and escape-learning method.
      
Firstly, considering the given key pose configurations in the form of unarticulated meshes in high dimensional space, we cast our motion in low dimensional space using the unsupervised learning method of locally linear embedding (LLE).
      
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low-frequency variability.
      
 

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