Claim · 1998 · publication · Stated in source · Supported

According to Burges (1998), Boser, Guyon and Vapnik (1992) showed that an older trick (Aizerman, 1964) generalises support vector methods to nonlinear decision functions.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

A Tutorial on Support Vector Machines for Pattern Recognition → attributes to → A training algorithm for optimal margin classifiers

Where it appears

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Claim and evidence

A Tutorial on Support Vector Machines for Pattern Recognitionattributes to ↓A training algorithm for optimal margin classifiers

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 1998 · publication Records dated 1998 →
Kind of attribution: Summarises an argument or finding of the work

Scope and limitsAttributed statement; the 1992 paper was not read.

The evidence 01

primary sourceSupports the claim

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery (Kluwer); copy hosted on an ENS Paris personal page · 1998

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“(Boser, Guyon and Vapnik, 1992), showed that a rather old trick (Aizerman, 1964) can be used to accomplish this”

Location in source PDF p. 16, section 4

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-31

Source details and check notes

Authors: Christopher J.C. Burges
Version: Retrieved for the candidate pack on 2026-10-29 (SHA-256 6095422e59a813e04ae5fea5248916c6ac87fc7c49ee1fb0b2fe3f8fced0379d). Pre-publication copy with the note "Appeared in: Data Mining and Knowledge Discovery 2, 121-167, 1998"; may differ from the journal text.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-29
Language: English (en)
Attribution: Christopher J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, 1998.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, Kinds of AI and technical history (research/technical-history). The quotation (19 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes, PDF line-break hyphens and Unicode compatibility forms, so the source’s typography can differ. The source role is primary for this record: the passage is the citing text’s own attribution, which is what the record states (corrected at admission; the pack had secondary, relative to the work credited). A cross-family machine check (31 October 2026, item 86) re-read the record’s sources and found that they support what the record states. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A document expressly attributes an argument, concept, term, finding or approach to another work, in its own words; the kind of attribution is recorded. The record is the citing document’s claim. It is not evidence that the cited work originated the idea, that the attribution is accurate, or of influence beyond the passage. Citation alone is recorded as a citation.

Date. Date of publication, as the source gives it.

Record ID th-stat-burges-credits-boser