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Showing 20 of 1,254 academic articles
Yu Zhang, Peter Tiňo B, Aleš Leonardis, Ke Tang • IEEE Transactions on Emerging Topics in Computational Intelligence
(2021)
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...
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Open Access
DOI: https://doi.org/10.1109/tetci.2021.3100641
Abhijit S. Pandya, Robert B. Macy • CRC Press eBooks
(2021)
From the Publisher:
The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...
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DOI: https://doi.org/10.1201/9780138744274
Pierre Baldi, Yves Chauvin • Neural Computation
(1993)
After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images...
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DOI: https://doi.org/10.1162/neco.1993.5.3.402
Alison Carling • Sigma Press eBooks
(1992)
A background to the history of neural networks is provided in this book, in a way accessible to readers without a formal knowledge of computer science or advanced biology. It describes the major learning algorithms, the constraints of neuro...
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Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks
(2001)
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...
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DOI: https://doi.org/10.1002/047084535x
N.K. Bose, Pengfei Liang (1995)
Part 1 Fundamentals: basics of neuroscience and artificial neuron models graphs algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm complexity of learning using feedforward networks adaptive structure networks. Part 3 Rec...
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Donald F. Specht • IEEE Transactions on Neural Networks
(1991)
A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with...
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DOI: https://doi.org/10.1109/72.97934
M.T. Hagan, H.B. Demuth • Proceedings of the ... American Control Conference/Proceedings of the American Control Conference
(1999)
Provides a quick overview of neural networks and explains how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorit...
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DOI: https://doi.org/10.1109/acc.1999.786109
Nicholas Carlini, David Wagner • IEEE Symposium on Security and Privacy (S&P)
(2017)
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...
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Open Access
DOI: https://doi.org/10.1109/sp.2017.49
Hervé Abdi • Journal of Biological Systems
(1994)
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...
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DOI: https://doi.org/10.1142/s0218339094000179
Marius-Constantin Popescu, Valentina Emilia Bălaş, Liliana Perescu-Popescu, Nikos E. Mastorakis (2009)
Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...
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DOI: https://doi.org/10.5555/1639537.1639542
C. H. Chen • McGraw-Hill, Inc. eBooks
(1996)
Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivi...
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Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics
(1997)
The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a ight control architecture based on feedback linearization of the aircraft...
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DOI: https://doi.org/10.2514/2.4029
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner et al. • IEEE Transactions on Neural Networks
(2008)
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...
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Open Access
DOI: https://doi.org/10.1109/tnn.2008.2005605
Chris Bishop • Choice Reviews Online
(1994)
From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...
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DOI: https://doi.org/10.5860/choice.31-5500
Allan M. Pinkus • Acta Numerica
(1999)
In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models....
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DOI: https://doi.org/10.1017/s0962492900002919
Dana H. Ballard • National Conference on Artificial Intelligence
(1987)
In the development of large-scale knowledge networks much recent progress has been inspired by connections to neurobiology. An important component of any neural network is an accompanying learning algorithm. Such an algorithm, to be biologi...
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Chris Bishop (1995)
Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes technique...
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DOI: https://doi.org/10.1093/oso/9780198538493.001.0001
Laurene V. Fausett • CERN Document Server (European Organization for Nuclear Research)
(1994)
1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...
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Amey Thakur • International Journal for Research in Applied Science and Engineering Technology
(2021)
The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an ...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2021.37362