Advances in Kernel Methods | MIT CogNetMachine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines SVMs , which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.
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Views Read Edit View history. Poggio, T. Improving the accuracy and speed of support vector learning machines. Download preview PDF.Neural Computation 7 2 : - Doktorarbeit, TU Berlin. In Haussier, D. Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel-based machine learning regime that explores the idea of using multiple mmethods for building better predictors.
NY: Wiley. Cambridge University Press. Given two objectives, utility maximization and privacy-loss minimizati. The multikernel stage uses the signal-to-noise ratio SNR score of each kernel to non-uniformly combine multiple compressive kernels.
All rights reserved. Skip to main content Skip to main navigation menu Skip to site footer. Abstract Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization.
Platt, J! Nonlinear Programming. To appear. Nonlinear component analysis as a kernel eigenvalue problem. B Phys.
Microsoft Research Cambridge UK. The word "kernel" is used in mathematics to denote a weighting function for a weighted sum or integral. Philadelphia: SIAM. The decision function for an SVM with quadratic polynomial kernel, Eq.
Cortes, C. Statistical Learning and Kernel Methods. Comparison of view-based object recognition algorithms using realistic 3D models. Need Help.Extracting support data for a given task. B Phys. The linear interpretation gives us insight about the algorithm. Skip to Main Content.
Neural Computation - Hidden categories: CS1 errors: missing periodical All articles with unsourced statements Articles with unsourced statements from October. In relation to the neural-network architecture, multi-kernel method can be described as a two-hidden-layered network with its width proportional to the number of kernels. B Phys.