22,283 research outputs found
Detecting Quasars in Large-Scale Astronomical Surveys
We present a classification-based approach to identify quasi-stellar radio
sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance
on a manually labeled training set. While reasonable results can already be
obtained via approaches working only on photometric data, our experiments
indicate that simple but problem-specific features extracted from spectroscopic
data can significantly improve the classification performance. Since our
approach works orthogonal to existing classification schemes used for building
the spectroscopic catalogs, our classification results are well suited for a
mutual assessment of the approaches' accuracies.Comment: 6 pages, 8 figures, published in proceedings of 2010 Ninth
International Conference on Machine Learning and Applications (ICMLA) of the
IEE
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance
Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two
challenging issues in support vector regression: first, the process of noisy
data; second, how to deal with the situation when the distribution of boundary
data is different from that of the overall data. The proposed e-DWSVR optimizes
the minimum margin and the mean of functional margin simultaneously to tackle
these two issues. In addition, we use both dual coordinate descent (CD) and
averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable
to large scale problems. We report promising results obtained by e-DWSVR in
comparison with existing methods on several benchmark datasets
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast
horizons. We also find that machine learning methods improve their
forecasting accuracy with respect to linear models as forecast horizons increase.
This results shows the suitability of SVR for medium and long term
forecasting.Peer ReviewedPostprint (published version
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