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Handling uncertainties in SVM classification

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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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Emilie Niaf (CREATIS), Rémi Flamary (LITIS), Carole Lartizien (CREATIS), Stéphane Canu (LITIS)

Machine Learning · Submitted 17 Jun 2011

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

The authors' main claim Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.

how it is stated · asserted 0.96 read the record →

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arXiv ID1106.3397 (version 1)
Pith ID27PEEO6B, Pith's permanent record of this paper and its review
CitationCarole Lartizien (CREATIS), Emilie Niaf (CREATIS), R\'emi Flamary (LITIS), St\'ephane Canu (LITIS). "Handling uncertainties in SVM classification." arXiv:1106.3397 (2011). https://pith.science/paper/27PEEO6B
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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-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,803 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.07 · human participants 0.05 · controlled comparison 0.73 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.93 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.

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 This paper addresses the pattern classification problem arising when available target data include some uncertainty information. background 1.00 0.24 0.03 asserted 0.90
s1 Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). background 0.81 0.16 0.02 asserted 1.00
s2 Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. contribution 0.99 0.92 0.51 asserted 1.00
s3 This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. method 0.88 0.85 0.05 asserted 1.00
s4 The solution provided can be used for both decision and posterior probability estimation. contribution 0.41 0.76 0.10 asserted 0.98
s5 Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances. result 1.00 0.94 0.66 asserted 0.96

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.70.

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
levels run
  • P abstract
not run
  • 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

This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.

Human review

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How the literature uses it

1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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