Performance of trained neural networks on clean data sets
Knowledge Extraction From Trained Neural Networks
... Artificial neural networks (ANN) are very efficient in solving various kinds of problems But Lack of explanation capability (Black box nature of Neural Networks) is one of the most important ...
Instantaneously trained neural networks with complex and quaternion inputs
... Abstract Neural network architectures such as backpropagation networks, perceptrons or generalized Hopfield networks can handle complex inputs but they require a large amount of time and resources ...
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Harnessing Pre Trained Neural Networks with Rules for Formality Style Transfer
... Bi-directional FT † 70.61 3.98 72.01 41.74 GPT-CAT 71.45 3.81 72.70 44.07 Table 3: Test performance on the E&M domain. PINC reflects the dissimilarity to the original informal sen- tence. Neither a too high nor a ...
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Parkinson’s Disease Voice Diagnosis System (PDVDS) Through Pso Trained Neural Networks
... ANN trained with ...values performance of ANN is below ...training data set is 1200 X 29 and initial minimum wait is ...whole data set with min-max function after we apply that data set ...
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Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks
... NARX neural networks to predict engine cylinder pressure traces using crank kinematics as input ...available. Neural networks are shown to be more appropriate than physical crank dynamic ...
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Decision Rule Extraction from Trained Neural Networks Using Rough Sets
... the trained neural ...rough sets; Section 4 describes our new method for rule extraction; Section 5 contains a experimental comparison using two data sets for the two symbolic mapping ...
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Classification of small data sets of images with transfer learning in convolutional neural networks
... speed. Neural networks are in a big expansion in a new ...Convolutional neural networks have been widely applied to a variety of pattern recognition problems, such as computer ...convolutional ...
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Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure
... ImageNet data set to achieve performance comparable to our baseline ...on performance, any comparison confounds architecture and ...we trained small instances of both architectures on just ...
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Challenging the Efficient Market Hypothesis with Dynamically Trained Artificial Neural Networks
... weekly data, however, does not support using a longer-term training history with ...training sets is more detrimental to accuracy/hit rate than are the informational limitations of shorter, more current ...
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Ensemble neural networks using interval neutrosophic sets and bagging
... three data sets named ionosphere, pima, and liver from UCI Machine Learning Repository are applied to our ...Each data set is split into a training set and a testing ...both sets are shown in ...
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NEURAL NETWORKS IN DATA MINING
... 39 Neural networks essentially comprise three pieces: the architecture or model; the learning algorithm; and the activation ...functions. Neural networks are programmed or “trained” to ...
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Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms
... of data modelling in which the collection of data are used to develop a model so that details of unknown aspects/states of the particular problem under consideration to be ...and trained with ...
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License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images
... test sets in section ...and trained models that work out of the box with the LPR pipeline without further calibration in section ...the performance on the Italian data set we created in ...
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Neural nets their use and abuse for small data sets
... a neural network, as suggested by Borggaard (1995), will obviously reduce this difficulty, but there is still a need for more powerful visualisation ...the neural net models failed to match the predictive ...
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Improving Post-Filtering of Artificial Speech Using Pre-Trained LSTM Neural Networks
... 3.3. Experiments Each of the five voices was parameterized, and the resulting set of vectors was divided into training, validation, and testing sets. The amount of data available for each voice is shown in ...
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Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.
... Artificial Neural Networks for distinguishing between different classes of ...Actual data from the ORFEO measurement campaign are used to assess the performance of the trained ...
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The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
... the performance of the current working back propagation algorithm which is Gradient Descent Method with Adaptive Gain by changing the momentum coefficient adaptively for each ...a neural network is ...
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Performance Analysis of Various Data Mining Classification Techniques using Neural Networks
... of data classification is maybe one of the most generally studied in data mining as well as machine learning ...of data classification includes a wide variety of different problems domains such as ...
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Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
... Chapter 3 Supply File Due to the differences between the SDNN and SNN, the conversion program creates a supply file along with a converted network file from every original SDNN. The network file is straightforward: Is ...
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Health Diagnosis Expert Advisory System on Trained Data Sets for Hyperthyroid
... using Trained Data Sets(TDS) and the results are compared by using suitable Data Matching Systems ...the data in the questionnaire form ...
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