16,349 research outputs found
Prediction of protein binding sites in protein structures using hidden Markov support vector machine
<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image
segmentation or human pose estimation. Training SSVMs, however, is
computationally costly, because it requires repeated calls to a structured
prediction subroutine (called \emph{max-oracle}), which has to solve an
optimization problem itself, e.g. a graph cut.
In this work, we introduce a new algorithm for SSVM training that is more
efficient than earlier techniques when the max-oracle is computationally
expensive, as it is frequently the case in computer vision tasks. The main idea
is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm
with efficient hyperplane caching, and (ii) use an automatic selection rule for
deciding whether to call the exact max-oracle or to rely on an approximate one
based on the cached hyperplanes.
We show experimentally that this strategy leads to faster convergence to the
optimum with respect to the number of requires oracle calls, and that this
translates into faster convergence with respect to the total runtime when the
max-oracle is slow compared to the other steps of the algorithm.
A publicly available C++ implementation is provided at
http://pub.ist.ac.at/~vnk/papers/SVM.html
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Cutting-plane methods are well-studied localization(and optimization)
algorithms. We show that they provide a natural framework to perform
machinelearning ---and not just to solve optimization problems posed by
machinelearning--- in addition to their intended optimization use. In
particular, theyallow one to learn sparse classifiers and provide good
compression schemes.Moreover, we show that very little effort is required to
turn them intoeffective active learning methods. This last property provides a
generic way todesign a whole family of active learning algorithms from existing
passivemethods. We present numerical simulations testifying of the relevance
ofcutting-plane methods for passive and active learning tasks.Comment: IJCNN 2015, Jul 2015, Killarney, Ireland. 2015,
\<http://www.ijcnn.org/\&g
Structured Learning of Tree Potentials in CRF for Image Segmentation
We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and
Learning System
- …