Journal ArticleDOI
Introduction to multi-layer feed-forward neural networks
TLDR
Examples of the use of multi-layer feed-forward neural networks for prediction of carbon-13 NMR chemical shifts of alkanes is given and advantages and disadvantages of multilayer feed- forward neural networks are discussed.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 1997-11-01. It has received 1206 citations till now. The article focuses on the topics: Time delay neural network & Types of artificial neural networks.read more
Citations
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Journal ArticleDOI
Subset Selection in Regression
TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
Journal ArticleDOI
Deep learning in bioinformatics
TL;DR: Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields, including bioinformatics as discussed by the authors, which has been emphasized in both academia and industry.
Journal ArticleDOI
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
TL;DR: A comprehensive survey of the recent research efforts on edge intelligence can be found in this paper, where the authors review the background and motivation for AI running at the network edge and provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the edge.
Journal ArticleDOI
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
TL;DR: In this article, the authors provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain.
Posted Content
Deep Learning in Bioinformatics
TL;DR: This review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies and suggest future research directions.
References
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A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.