neural net
Boundary reconstruction process of a TV-based neural net without prior conditions
... During the back-propagation process, the network must iteratively minimize a regularized error function which we will set to the expression (12) in the following sections. Since the trunc{·} operator is involved in those ...
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An MLP Neural Net with L1 and L2 Regularizers for Real Conditions of Deblurring
... proposed neural net is able to adapt to the local nature of the problem and achieve very similar results in the truncated model to those obtained by the two other ...
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An integrated epidemiological and neural net model of the warfarin effect in managed care patients
... Methods: We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their ...
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Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications
... a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal ...self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain ...
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Rule based vs Neural Net Approaches to Semantic Textual Similarity
... a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks ...Bi-LSTM neural network system that solely takes word-level ...
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Evolutive Neural Net Fuzzy Filtering: Basic Description
... The paper was about the analysis of the evolutive neural net fuzzy filtering and its real time conditions, in order show the applicability conditions into dynamical systems. The paper describes the adaptive ...
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The Context Dependent Additive Recurrent Neural Net
... our neural net archi- tectures, CARNN-based systems outperform pre- vious methods on several public datasets for di- alog (Frame and Babi Task 6), question answer- ing (TrecQA) and contextual language ...
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Rainfall Prediction using Neural Net based Frequency Analysis Approach
... Model is trained for rainfall prediction of each year of Gujarat (21) from 1996 to 2010. Model input for each year prediction is rainfall data from all the years prior to the prediction year. A separate neural ...
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Implementation of Backpropagation Algorithm: A Neural Net- work Approach for Pattern Recognition
... A neural network model is a powerful tool used for various real life applications like time series predication, sequence detection, data filtering, pattern recognition and other intelligent tasks as performed by ...
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A Novel Neural Net based Off-line English Character Recognition System
... Perception neural network with back propagation functions has been ...propagation neural networks are able to recover unknown data (correct data entry) from the ...
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Features and neural net recognition strategies for hand printed digits
... List of Tables Table 3-1 Basic Morphological Operators 36 Table 5-1 Variables Adjusted for All Steps 73 Table 5-2 Sample Table 5-3 Neural Network Test Settings 76 Table 5-4 Central Momen[r] ...
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FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions
... Explaining the decisions of a Deep Learning Network is im- perative to safeguard end-user trust. Such explanations must be intuitive, descriptive, and faithfully explain why a model makes its decisions. In this work, we ...
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Comprehensive study & implementation of energy prognosis using neural net approach
... forecasting (LTLF). Short-term load forecasts, of which the forecast horizon is up to two weeks, are primarily used in power systems operations, such as unit commitment and economic dispatch. Long-term load forecasts, of ...
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Improving three layer neural net convergence
... works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.. We first observe that if ther[r] ...
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Memory Capacity of a novel optical neural net architecture
... memory neural nets are useful for pattern recognition and for associating the recognized patterns with corresponding output ...[5] net storing orthogonal codes in the intermediate layer to minimise the ...
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Neural Net Models of Open domain Discourse Coherence
... (3) Foltz et al. (1998) computes the semantic relatedness of two text units as the cosine similarity between their LSA vectors. The coherence of a discourse is the average of the cosine of adjacent sentences. We used ...
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Comparison of Diverse Decoding Methods from Conditional Language Models
... Conditional neural language models, which train a neural net to map from one sequence to an- other, have had enormous success in natural lan- guage processing tasks such as machine transla- tion ...
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An Introduction to Pattern Recognition Michael Alder pdf
... If we normalise into, say, an 11 by 9 array, we can rewrite the characters into standard form. Then we could, if desperate for ideas, take each character as a point in . This is not a good idea, although it has been ...
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Download Download PDF
... using neural net- works in the field of ...used neural networks to predict burr ...using neural networks is described below: Sudhakaran (1999) proposed a neural net- work model ...
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Neural Generative Question Answering
... a neural network to achieve a desired precision and cover- age in real world ...The neural net- work, and more generally the fully distributed way of representation, is good at representing smooth ...
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