Machine Learning
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
IEEE Computational Science & Engineering
Training products of experts by minimizing contrastive divergence
Neural Computation
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A fast learning algorithm for deep belief nets
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Learning ensemble classifiers via restricted Boltzmann machines
Pattern Recognition Letters
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We compare the recently proposed Discriminative Restricted Boltzmann Machine (DRBM) to the classical Support Vector Machine (SVM) on a challenging classification task consisting in identifying weapon classes from audio signals. The three weapon classes considered in this work (mortar, rocket, and rocket-propelled grenade), are difficult to reliably classify with standard techniques because they tend to have similar acoustic signatures. In addition, specificities of the data available in this study make it challenging to rigorously compare classifiers, and we address methodological issues arising from this situation. Experiments show good classification accuracy that could make these techniques suitable for fielding on autonomous devices. DRBMs appear to yield better accuracy than SVMs, and are less sensitive to the choice of signal preprocessing and model hyperparameters. This last property is especially appealing in such a task where the lack of data makes model validation difficult. (10Roughly speaking, the number of DOF in the regression residuals is computed as the number of observations in the training set minus the number of parameters that are part of the regression model. © 2012 Wiley Periodicals, Inc.)