22,000 research outputs found
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
Microbial identification is a central issue in microbiology, in particular in
the fields of infectious diseases diagnosis and industrial quality control. The
concept of species is tightly linked to the concept of biological and clinical
classification where the proximity between species is generally measured in
terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the
information provided by this well-known hierarchical structure is rarely used
by machine learning-based automatic microbial identification systems.
Structured machine learning methods were recently proposed for taking into
account the structure embedded in a hierarchy and using it as additional a
priori information, and could therefore allow to improve microbial
identification systems. We test and compare several state-of-the-art machine
learning methods for microbial identification on a new Matrix-Assisted Laser
Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset.
We include in the benchmark standard and structured methods, that leverage the
knowledge of the underlying hierarchical structure in the learning process. Our
results show that although some methods perform better than others, structured
methods do not consistently perform better than their "flat" counterparts. We
postulate that this is partly due to the fact that standard methods already
reach a high level of accuracy in this context, and that they mainly confuse
species close to each other in the tree, a case where using the known hierarchy
is not helpful
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
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