BACKGROUND Medical educators have used the hidden curriculum concept for over three decades to make visible the effects of tacit learning, including how culture, structures, and institutions influence professional identity formation. In response to calls to see more humanistic-oriented training in medicine, the authors examined how the hidden curriculum construct has been applied in the English...

Our living creatures represent global information in their brain by integrating local sensory signals such as visual sensory signals. In this paper, the state of hidden layer in a layered neural network with local inputs after learning was observed for some cases. Some characters became clear as follows. (1)If the training signal changes gradually in space, the hidden layer becomes to represent...

Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropri...

This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection ...

Local Coupled Extreme Learning Machine (LCELM) is a recently-proposed variant of ELM, which assigns an address for each hidden-layer node and activates the hidden-layer node when its activated degree is less than a given threshold. In this paper, an improved version of LCELM is proposed by developing a new way to initialize the address for each hidden-layer node and calculating the activated de...

We derive an expectation maximization algorithm for maximum-likelihood training of substitution rate matrices from multiple sequence alignments. The algorithm can be used to train hidden substitution models, where the structural context of a residue is treated as a hidden variable that can evolve over time. We used the algorithm to train hidden substitution matrices on protein alignments in the...

Several neural network architectures have been developed over the past several years. One of the most popular and most powerful architectures is the multilayer perceptron. This architecture will be described in detail and recent advances in training of the multilayer perceptron will be presented. Multilayer perceptrons are trained using various techniques. For years the most used training metho...

A multiple smooth model is proposed by smoothing technique and piecewise technique for large scale data. Mapping the training data to the hidden space with a hidden function, the proposed model divides the original data into several subclasses by Fuzzy C Means (FCM), whose initial cluster centers are selected by samples with large density indexes; derives the smooth differentiable model by util...

This paper presents a new algorithm for constructing and training wavelet neural network. This algorithm is based on the variation of the number of hidden neurons dynamically during the training process. The suggested method determines the optimal number of the hidden neurons and solves the optimization problem of wavelet neural network structure. The problem of finding a good neural model is t...

It has been shown that a nonmonotone neural network model can recognize spatiotemporal patterns without expanding them into spatial patterns. We improve the recognition ability of this model by introducing hidden neurons. We also show a simple method of training the hidden neurons. Computer simulation shows that this model can recognize complicated spatiotemporal patterns.