Elements of artificial neural networks: By Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka. MIT Press, Cambridge, MA. (1997). 344 pages. $55.00doi:10.1016/S0898-1221(97)82926-4ELSEVIERComputers & Mathematics with Applications
This text is suitable for use in graduate courses on machine learning. Researchers and students in artificial intelligence, cognitive science, and statistics will find it a useful and informative addition to their libraries. Elements of Machine Learning 2024 pdf epub mobi 电子书 ...
Artificial neural networks (ANNs), including RBF models, have emerged as superior alternatives to multilinear regression in water quality simulation and prediction. A study by emphasized the capability of ANNs, such as RBFs, to offer valuable insights into the linearity and nonlinearity of input and ...
Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016). Article CAS PubMed Google Scholar Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci...
The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, ...
View PDFView articleView in ScopusGoogle Scholar [43] M.H. Ahmadi, H. Jashnani, K.W. Chau, R. Kumar, M.A. Rosen Carbon dioxide emissions prediction of five middle eastern countries using artificial neural networks Energy Sources Part A, 2019 (2019), Article 1679914 Google Scholar [44] ...
Smakman JGJ, Stavenga DG: Angular Sensitivity of Blowfly Photoreceptors - Broadening by Artificial Electrical Coupling. J Comp Physiol A. 1987, 160 (4): 501-507. 10.1007/BF00615083. Article Google Scholar Hille B: Ion channels of excitable membranes. 2001, Sunderland, MA: Sinauer Associates...
Next, we compared single-feature logistic regression models with an artificial neural network (NN) model that integrates multiple hotspot features. Artificial neural networks utilize a machine-learning approach that effectively enables the integration of multiple regression models, each of which is transfor...
This book is designed for researchers and students in a broad variety of fields: statistics, artificial intelligence, engineering, finance and others. We expect that the reader will have had at least one elementary course in statistics, covering basic topics including linear regression. ...
artificial neural networks, deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks, stacked autoencoders, deep Boltzmann machines, deep belief networks, transformers, language models), dimensionality reduction algorithms, time series forecast...