3,297 research outputs found
Customizing kernel functions for SVM-based hyperspectral image classification
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut
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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications
Business optimization is becoming increasingly important because all business
activities aim to maximize the profit and performance of products and services,
under limited resources and appropriate constraints. Recent developments in
support vector machine and metaheuristics show many advantages of these
techniques. In particular, particle swarm optimization is now widely used in
solving tough optimization problems. In this paper, we use a combination of a
recently developed Accelerated PSO and a nonlinear support vector machine to
form a framework for solving business optimization problems. We first apply the
proposed APSO-SVM to production optimization, and then use it for income
prediction and project scheduling. We also carry out some parametric studies
and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband
Model Selection for Support Vector Machine Classification
We address the problem of model selection for Support Vector Machine (SVM)
classification. For fixed functional form of the kernel, model selection
amounts to tuning kernel parameters and the slack penalty coefficient . We
begin by reviewing a recently developed probabilistic framework for SVM
classification. An extension to the case of SVMs with quadratic slack penalties
is given and a simple approximation for the evidence is derived, which can be
used as a criterion for model selection. We also derive the exact gradients of
the evidence in terms of posterior averages and describe how they can be
estimated numerically using Hybrid Monte Carlo techniques. Though
computationally demanding, the resulting gradient ascent algorithm is a useful
baseline tool for probabilistic SVM model selection, since it can locate maxima
of the exact (unapproximated) evidence. We then perform extensive experiments
on several benchmark data sets. The aim of these experiments is to compare the
performance of probabilistic model selection criteria with alternatives based
on estimates of the test error, namely the so-called ``span estimate'' and
Wahba's Generalized Approximate Cross-Validation (GACV) error. We find that all
the ``simple'' model criteria (Laplace evidence approximations, and the Span
and GACV error estimates) exhibit multiple local optima with respect to the
hyperparameters. While some of these give performance that is competitive with
results from other approaches in the literature, a significant fraction lead to
rather higher test errors. The results for the evidence gradient ascent method
show that also the exact evidence exhibits local optima, but these give test
errors which are much less variable and also consistently lower than for the
simpler model selection criteria
Solving Lattice QCD systems of equations using mixed precision solvers on GPUs
Modern graphics hardware is designed for highly parallel numerical tasks and
promises significant cost and performance benefits for many scientific
applications. One such application is lattice quantum chromodyamics (lattice
QCD), where the main computational challenge is to efficiently solve the
discretized Dirac equation in the presence of an SU(3) gauge field. Using
NVIDIA's CUDA platform we have implemented a Wilson-Dirac sparse matrix-vector
product that performs at up to 40 Gflops, 135 Gflops and 212 Gflops for double,
single and half precision respectively on NVIDIA's GeForce GTX 280 GPU. We have
developed a new mixed precision approach for Krylov solvers using reliable
updates which allows for full double precision accuracy while using only single
or half precision arithmetic for the bulk of the computation. The resulting
BiCGstab and CG solvers run in excess of 100 Gflops and, in terms of iterations
until convergence, perform better than the usual defect-correction approach for
mixed precision.Comment: 30 pages, 7 figure
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
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