36,535 research outputs found
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
We consider the problem of discriminative factor analysis for data that are
in general non-Gaussian. A Bayesian model based on the ranks of the data is
proposed. We first introduce a new {\em max-margin} version of the
rank-likelihood. A discriminative factor model is then developed, integrating
the max-margin rank-likelihood and (linear) Bayesian support vector machines,
which are also built on the max-margin principle. The discriminative factor
model is further extended to the {\em nonlinear} case through mixtures of local
linear classifiers, via Dirichlet processes. Fully local conjugacy of the model
yields efficient inference with both Markov Chain Monte Carlo and variational
Bayes approaches. Extensive experiments on benchmark and real data demonstrate
superior performance of the proposed model and its potential for applications
in computational biology.Comment: 14 pages, 7 figures, ICML 201
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