95,840 research outputs found
Cholesky-factorized sparse Kernel in support vector machines
Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its convex optimization formulation and handling non-linear classification. However, one of its main drawbacks is the long time it takes to train large data sets. This limitation is often aroused when applying non-linear kernels (e.g. RBF Kernel) which are usually required to obtain better separation for linearly inseparable data sets. In this thesis, we study an approach that aims to speed-up the training time by combining both the better performance of RBF kernels and fast training by a linear solver, LIBLINEAR. The approach uses an RBF kernel with a sparse matrix which is factorized using Cholesky decomposition. The method is tested on large artificial and real data sets and compared to the standard RBF and linear kernels where both the accuracy and training time are reported. For most data sets, the result shows a huge training time reduction, over 90\%, whilst maintaining the accuracy
Algorithm and performance of a clinical IMRT beam-angle optimization system
This paper describes the algorithm and examines the performance of an IMRT
beam-angle optimization (BAO) system. In this algorithm successive sets of beam
angles are selected from a set of predefined directions using a fast simulated
annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on
each generated set of beams. The IMRT optimization is accelerated by using a
fast dose calculation method that utilizes a precomputed dose kernel. A compact
kernel is constructed for each of the predefined beams prior to starting the
FSA algorithm. The IMRT optimizations during the BAO are then performed using
these kernels in a fast dose calculation engine. This technique allows the IMRT
optimization to be performed more than two orders of magnitude faster than a
similar optimization that uses a convolution dose calculation engine.Comment: Final version that appeared in Phys. Med. Biol. 48 (2003) 3191-3212.
Original EPS figures have been converted to PNG files due to size limi
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
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