unsupervised neural network model
Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings
... Artificial Neural Network (ANN) model is used for the analysis of any type of con- ventional building frame under an arbitrary loading in terms of the rotational end moments of its ...
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Unsupervised Neural Network Naive Bayes Model for Grouping Data Regional Development Results
... the model, where, first, the data will be evaluated using the SOM-NN to produce clusters that will become targets for learning Class performed on the same data when using naive ...
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Application of artificial neural networks for understanding and diagnosing the state of mastitis in dairy cattle
... and unsupervised ANN and LDA for the classification of CM and healthy ...(MLP) network (a type of supervised ANN), sensitivity was 84% and specificity was ...(SOFM) model (a type of ...
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On line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
... learning model using Gaussian process classifica- tion and an unsupervised neural network-based di- alogue embedding to enable truly on-line policy learning in spoken dialogue ...proposed ...
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A Common Neural Network Model for Unsupervised Exploratory Data Analysis and Independent Component Analysis
... an unsupervised learning algorithm, which enables the identification and visualisation of latent structure within ensembles of high dimensional ...for unsupervised exploratory data analysis and data ...
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Domain Adaptive Model For Sentiment Classification Using Deep Learning Approach
... adaptive model for opinion mining and sentiment classification of unstructured text reviews posted in social media site or web ...them. Model proposed in the paper utilizes deep learning techniques for ...
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A Hybrid Data Mining Model to Improve Customer Response Modeling in Direct Marketing
... effective model has always been a challenging issue in response ...bagging neural network on the training set and evaluated the model on the test ...and unsupervised learning to build a ...
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An Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network
... Recurrent Neural Networks [27-29] in clustering which use unsupervised learning methods, these methods introduce themselves as a useful instrument in control and ...to model dynamical systems and ...
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Unsupervised Recurrent Neural Network Grammars
... Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, ...
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Diagnosis of Breast Cancer Using Intelligent Techniques
... Artificial Neural Network (ANN) ,unsupervised Artificial Neural Network ,Statistical and decision tree based have been applied to classify data related to breast cancer health care ...
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UAV detection : a STDP trained deep convolutional spiking neural network retina-neuromorphic approach
... sparse spiking neuron model which only further sparsifies throughout the net- work, while in an unsupervised fashion learns distinctive feature to identify a UAV. This sparsity instils the ethos of only ...
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Deep Temporal Recurrent Replicated Softmax for Topical Trends over Time
... novel unsupervised neural dy- namic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNN- RSM), where the discovered topics at each time influence the ...
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Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... 2004). According to the efficient market hypothesis, prices in the stock market follow a random walk process. Because of the fast flow of information in the market and its impact on the stock price, stock return cannot ...
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New variant of the Self Organizing Map in Pulsed Neural Networks to Improve Phoneme Recognition in Continuous Speech
... for unsupervised sequence processing for temporal data [22] ...MSOM model combines a noise-tolerant learning architecture which implements a compact back-reference to the previous winner with separately ...
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BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION
... builds model after few sample data accumulating, which can weaken the randomness of the original data, find the data conversion rule, and finally realize forecast ...this model. Neural network ...
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Glyph aware Embedding of Chinese Characters
... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan ...
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Osmotic Drying Rate Estimation for Dehydration of Beetroot Slices using Artificial Neural Network
... Osmotic dehydration can be viewed as an alternative method for drying of food materials with advantages of retention of gloss, texture & colour of dried products. Artificial neural network is emerging ...
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Development and Evaluation of Advanced Classification Systems using Remotely Sensed Data for Accurate Land-Use/Land-Cover Mapping
... KSOM-SA network has the potential to overcome the local minima problem and thus improve the classification performance as compared with the standard KSOM ...MLP network and the two unsupervised KSOM ...
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Predicting heat stressed EEG spectra by self organising feature map and learning vector quantizers——SOFM and LVQ based stress prediction
... the network was simulated number of times and the per- formance was calculated for some of the simulations employing two decay functions of learning rate and neighborhood size, three neighborhood tapering schemes, ...
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Blind Navigation System using Artificial Intelligence
... artificial neural networks (SIANN), due to their shared-weight architecture and translation invariance ...convolutional neural network can achieve reasonable performance on hard visual recognition ...
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