Netlab: Algorithms for Pattern Recognition

By I. Nabney. 2002

1,063 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0a42ff0d328fc08f5a7867b89a39c4179ba28297

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Citations in the corpus

Automatic Classification of Acoustic Emission Patterns (2003, methodology): “The information ‘learnt’ from the training data can then be used to make a decision on classification when testing data is presented [10].”
MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio (2007, methodology): “Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multi-channel signals, etc.”
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities (2006, methodology): “Unfortunately, it is not possible to obtain x ed point equations for the hyperparameters , since they appear both in the prior mean and in the prior covariance for the concentrations . We optimised them using a scaled conjugate gradient algorithm in the NETLAB implementation of Nabney (2002) .”
Global crustal thickness from neural network inversion of surface wave data (2007, background): “This ensures faster convergence and avoids ending up in poor local minima (Nabney 2002).”
Algorithm runtime prediction: Methods & evaluation (2012, methodology): “We thus employed the popular Matlab neural network package NETLAB [84].”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “In fact, focusing on their structures, RBF networks are typically built with one hidden layer with non-linear activation functions and a linear output layer, in order to maintain a fast training procedure [21].”
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation (2011, methodology): “The initialization of the EM algorithm [32] can influence the resulting GMM, so we repeated the cross-validation ten times for each number of components and each camera.”
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data (2003, methodology): “These gradients may be used in combinatio n w th (2) in a non-linear optimiser such as scaled conjugate gradients (S CG) [7] to obtain a latent variable representation of the data.”
Damage classification in carbon fibre composites using acoustic emission: A comparison of three techniques (2015, methodology): “Described in greater detail by Nabney [22], the technique consists of interrogating multi-dimensional data to locate the two planes with maximum scatter, these give the principal components.”