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Asymptotics of Gaussian Regularized Least-Squares
(2005-10-20)
We consider regularized least-squares (RLS) with a Gaussian kernel. Weprove that if we let the Gaussian bandwidth $\sigma \rightarrow\infty$ while letting the regularization parameter $\lambda\rightarrow 0$, the RLS solution ...
Neural Networks
(1996-03-13)
We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...
Learning World Models in Environments with Manifest Causal Structure
(1995-05-05)
This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" ...
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
(1996-12-01)
The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special ...
Nonparametric Sparsity and Regularization
(2011-09-26)
In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...
Multi-Class Learning: Simplex Coding And Relaxation Error
(2011-09-27)
We study multi-category classification in the framework of computational learning theory. We show how a relaxation approach, which is commonly used in binary classification, can be generalized to the multi-class setting. ...
AvatarSAT: An Auto-tuning Boolean SAT Solver
(2009-08-26)
We present AvatarSAT, a SAT solver that uses machine-learning classifiers to automatically tune the heuristics of an off-the-shelf SAT solver on a per-instance basis. The classifiers use features of both the input and ...
Multiscale Geometric Methods for Data Sets I: Multiscale SVD, Noise and Curvature
(2012-09-08)
Large data sets are often modeled as being noisy samples from probability distributions in R^D, with D large. It has been noticed that oftentimes the support M of these probability distributions seems to be well-approximated ...
Cicada: Predictive Guarantees for Cloud Network Bandwidth
(2014-03-24)
In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know ...
Elastic-Net Regularization in Learning Theory
(2008-07-24)
Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie ["Regularization and variable selection via the elastic net" J. R. Stat. ...