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Results 1 - 10
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9,081
Online Learning with Kernels
, 2003
"... Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little u ..."
Abstract
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Cited by 2831 (123 self)
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and computationally efficient algorithms for a wide range of problems such as classification, regression, and novelty detection. In addition to allowing the exploitation of the kernel trick in an online setting, we examine the value of large margins for classification in the online setting with a drifting target. We
Efficient Additive Kernels via Explicit Feature Maps
"... Maji and Berg [13] have recently introduced an explicit feature map approximating the intersection kernel. This enables efficient learning methods for linear kernels to be applied to the non-linear intersection kernel, expanding the applicability of this model to much larger problems. In this paper ..."
Abstract
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Cited by 245 (9 self)
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we generalize this idea, and analyse a large family of additive kernels, called homogeneous, in a unified framework. The family includes the intersection, Hellinger’s, and χ2 kernels commonly employed in computer vision. Using the framework we are able to: (i) provide explicit feature maps for all
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
- ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
Abstract
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Cited by 1051 (0 self)
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. However, training with a maximum likelihood score will produce non-sparse kernel machines. Instead, we train an SVM, then train the parameters of an additional sigmoid function to map the SVM outputs into probabilities. This chapter compares classification error rate and likelihood scores for an SVM plus
A tutorial on support vector machines for pattern recognition
- Data Mining and Knowledge Discovery
, 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
Abstract
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Cited by 3393 (12 self)
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large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vect ..."
Abstract
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Cited by 966 (5 self)
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functions than a comparable SVM while offering a number of additional advantages. These include the benefits of probabilistic predictions, automatic estimation of `nuisance’ parameters, and the facility to utilise arbitrary basis functions (e.g. non-`Mercer’ kernels). We detail the Bayesian framework
Maté: A Tiny Virtual Machine for Sensor Networks
, 2002
"... Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu-late. ..."
Abstract
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Cited by 510 (21 self)
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capsules enable the deploy-ment of ad-hoc routing and data aggregation algorithms. Maté's concise, high-level program representation simplifies programming and allows large networks to be frequently re-programmed in an energy-efficient manner; in addition, its safe execution environment suggests a use
Safe Kernel Extensions Without Run-Time Checking
- Proc. of OSDI'96
"... Abstract This paper describes a mechanism by which an operating system kernel can determine with certainty that it is safe to execute a binary supplied by an untrusted source. The kernel first defines a safety policy and makes it public. Then, using this policy, an application can provide binaries i ..."
Abstract
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Cited by 429 (20 self)
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in a special form called proof-carrying code, or simply PCC. Each PCC binary contains, in addition to the native code, a formal proof that the code obeys the safety policy. The kernel can easily validate the proof without using cryptography and without consulting any external trusted entities
NeTra: A toolbox for navigating large image databases
- Multimedia Systems
, 1999
"... . We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robu ..."
Abstract
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Cited by 382 (15 self)
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robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image
An Empirical Study of Operating System Errors
, 2001
"... We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previ-ous studies that consider errors found by manual inspec-tion of logs, testing, and surveys because static analysis is applied uniforml ..."
Abstract
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Cited by 363 (9 self)
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uniformly to the entire kernel source, though our approach necessarily considers a less comprehensive variety of errors than previous studies. In addition, au-tomation allows us to track errors over multiple versions of the kernel source to estimate how long errors remain in the system before they are fixed
Parity-Based Loss Recovery for Reliable Multicast Transmission
"... We investigate how FEC (Forward Error Correction) can be combined with ARQ (Automatic Repeat Request) to achieve scalable reliable multicast transmission. We consider the two scenarios where FEC is introduced as a transparent layer underneath a reliable multicast layer that uses ARQ, and where FEC a ..."
Abstract
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Cited by 335 (19 self)
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and ARQ are both integrated into a single layer that uses the retransmission of parity data to recover from the loss of original data packets. Toevaluate the performance improvements due to FEC, we consider different types of loss behaviors (spatially or temporally correlated loss, homogeneous
Results 1 - 10
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9,081
