44,096 research outputs found
An ontology enhanced parallel SVM for scalable spam filter training
This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart
On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
With the coming data deluge from synoptic surveys, there is a growing need
for frameworks that can quickly and automatically produce calibrated
classification probabilities for newly-observed variables based on a small
number of time-series measurements. In this paper, we introduce a methodology
for variable-star classification, drawing from modern machine-learning
techniques. We describe how to homogenize the information gleaned from light
curves by selection and computation of real-numbered metrics ("feature"),
detail methods to robustly estimate periodic light-curve features, introduce
tree-ensemble methods for accurate variable star classification, and show how
to rigorously evaluate the classification results using cross validation. On a
25-class data set of 1542 well-studied variable stars, we achieve a 22.8%
overall classification error using the random forest classifier; this
represents a 24% improvement over the best previous classifier on these data.
This methodology is effective for identifying samples of specific science
classes: for pulsational variables used in Milky Way tomography we obtain a
discovery efficiency of 98.2% and for eclipsing systems we find an efficiency
of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is
superior to other machine-learned methods in terms of accuracy, speed, and
relative immunity to features with no useful class information; the RF
classifier can also be used to estimate the importance of each feature in
classification. Additionally, we present the first astronomical use of
hierarchical classification methods to incorporate a known class taxonomy in
the classifier, which further reduces the catastrophic error rate to 7.8%.
Excluding low-amplitude sources, our overall error rate improves to 14%, with a
catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427
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