8,416 research outputs found
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
Building Wavelet Histograms on Large Data in MapReduce
MapReduce is becoming the de facto framework for storing and processing
massive data, due to its excellent scalability, reliability, and elasticity. In
many MapReduce applications, obtaining a compact accurate summary of data is
essential. Among various data summarization tools, histograms have proven to be
particularly important and useful for summarizing data, and the wavelet
histogram is one of the most widely used histograms. In this paper, we
investigate the problem of building wavelet histograms efficiently on large
datasets in MapReduce. We measure the efficiency of the algorithms by both
end-to-end running time and communication cost. We demonstrate straightforward
adaptations of existing exact and approximate methods for building wavelet
histograms to MapReduce clusters are highly inefficient. To that end, we design
new algorithms for computing exact and approximate wavelet histograms and
discuss their implementation in MapReduce. We illustrate our techniques in
Hadoop, and compare to baseline solutions with extensive experiments performed
in a heterogeneous Hadoop cluster of 16 nodes, using large real and synthetic
datasets, up to hundreds of gigabytes. The results suggest significant (often
orders of magnitude) performance improvement achieved by our new algorithms.Comment: VLDB201
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