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Very Fast Kernel SVM under Budget Constraints

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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David Picard

Machine Learning · Submitted 31 Dec 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show in the experiment that our algorithm is able to achieve high accuracy while having a very high number of samples processed per second both in training and in the evaluation.

how it is stated · asserted 1.00 read the record →

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arXiv ID1701.00167 (version 1)
Pith IDMIGUNREQ, Pith's permanent record of this paper and its review
CitationDavid Picard. "Very Fast Kernel SVM under Budget Constraints." arXiv:1701.00167 (2016). https://pith.science/paper/MIGUNREQ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,506 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.20 · human participants 0.04 · controlled comparison 0.07 · randomized assignment 0.02 · states quantitative result 0.08 · compares with prior results 0.11 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in the experiment that our algorithm is able to achieve high accuracy while having a very high number of samples processed per second both in training and in the evaluation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. contribution 1.00 0.72 0.13 asserted 0.92
s1 We propose to split the input space using LVQ and train a Kernel SVM in each cluster. method 0.89 0.60 0.04 asserted 0.84
s2 To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. method 0.93 0.61 0.05 asserted 0.61
s3 We show in the experiment that our algorithm is able to achieve high accuracy while having a very high number of samples processed per second both in training and in the evaluation. result 1.00 0.96 0.77 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in the experiment that our algorithm is able to achieve high accuracy while having a very high number of samples processed per second both in training and in the evaluation.

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