An introduction to computing with neural nets

By Richard P. Lippmann. 1987

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Citations in the corpus

On the computational power of neural nets (1992, background): “This function has appeared in many applications of neural nets (e.g. [2, 4, 15 , 30]).”
Cerebellar cortex: its simulation and the relevance of Marr's theory. (1992, background): “The following items are required for a complete specification of the model (Lippmann, 1987): (i) the net topology (i.”
An assessment of support vector machines for land cover classification (2002, background): “As a result, many types of neural networks have been developed ( Lippman 1987 ); the most widely used in the classie cation of remotely sensed images is a group of networks called a multi-layer perceptron (MLP) (e.g.”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “Arti ﬁ cial neural networks ( ANN ) is a ﬂ exible and powerful family of machine learning methods, originally inspired by biological neural networks in animal brains (Lippmann, 1987; McCulloch & Pitts, 1943).”
Are artificial neural networks black boxes? (1997, background): “Many different models of neural nets have been proposed [ 6 ], [7], however, multilayered feedforward ANN’s are especially interesting since they are the most common.”
Integration of artificial neural networks and fuzzy Delphi for stock market forecasting (1996, methodology): “Among the different network structures and learning algorithms, this research selects the feedforward ANN with EBP learning algorithm since it can predict the continuous output and has been applied in a variety of areas, like control and pattern recognition (Lippmann 1987).”
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks (2019, methodology): “ANN has been employed in many disciplines [31] due to its model-independent and computationally efﬁcient properties [31], [32].”
Noise reduction using connectionist models (1988, methodology): “A four-layer feed-forward network was chosen as an architecture as it can realize in principle any mapping function[ 6 ].”
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (2009, background): “However, there is rarely an advantage in using more than one hidden layer (Lippmann, 1987; Rumelhart et al., 1986).”
A review on design, modeling and applications of computer experiments (2006, background): “Promotion of the use of OAs is in large part due to the work of (Taguchi, 1978, 1987) in quality engineering.”
Importance of input data normalization for the application of neural networks to complex industrial problems (1997, methodology): “In both cases supervised training with multilayer perceptron are used [2].”