This paper investigates how behavioral cloning can be used to decrease training time for students learning to y on simulators. The challenges presented to each student must be tailored to their unique learning experiences. This requires an intelligent training regime that exploits a model of each student that predicts where the student's performance will be de cient. Here we show that cloning t...

Yapay sinir ağı (YSA) tarafından gerçekleştirilen hesaplama, birbiriyle iletişim kurmadan girdi uzayı üzerinde çalışan iki veya daha fazla modüle (alt sistemler) ayrıştırılabiliyorsa, modülerdir (MYSA). Modülerlik, karmaşık bir hesaplama görevini basit görevlere bölerek uzayının farklı bölgelerini öğrenip uzmanlaşma eğilimindeki modüllerin bireysel çözümlerini birleştirme yaparak çözüme izin ve...

Can we understand the dynamic behaviour of leaky integrate-and-fire (LIF) networks, which present the major, and possibly the only, analytically tractable tool we employ in computational neuroscience? To answer this question, here we present a theoretical framework on the spike activities of LIF networks by including the first order moment (mean firing rate) and the second order moment statisti...

This paper discusses a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange and analysis of intemal representation. It is based on modular neural networks[l][2]. We developed a number of learning algorithms and prediction methods for the TOPIX(Toky0 Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions and the simulat...

We here compare the performance (predictive accuracy and processing time) of different neural network ensembles with that of nearest neighbor classifier ensembles. Concerning the connectionist models, the multilayer perceptron and the modular neural network are employed. Experiments on several real-problem data sets demonstrate a certain superiority of the nearest-neighborbased schemes, in term...

Ore grade estimation is a key aspect in the evaluation of a mineral deposit. In this paper an alternative approach to currently applied methods of ore grade estimation is presented. This alternative approach involves a modular neural network system integrated in a state of the art 3D resource modelling package. The need for a new method of ore grade estimation comes from the difficulties in app...

In this paper, a new powerful method in artificial neural networks, called modular network SOM (mnSOM) is introduced. mnSOM is a generalization of Self Organizing Maps (SOM) formed by replacing each vector unit of SOM with function module. The modular function could be a multi layer perceptron, a recurrent neural network or even SOM itself. Having this flexibility, mnSOM becomes a new powerful ...

Nowadays, the Multiple Classification Systems (MCS) (also called as ensemble of classifiers, committee of learners and mixture of experts) constitutes a well-established research field in Pattern Recognition and Machine Learning. The MCS consists in dividing the whole problem with resampling methods, or using different models for constructing the system over a single data set. A similar approac...

Biometric Identification is a very old field where we try to identify people by their biometric identities. The field shifted to bi-modal systems where more than one modality was used for the identification purposes. The bimodal systems face problem related to high dimensionality that may many times result in problems. The individual modules already have large dimensionality. Their fusion adds ...