A survey on semi-supervised learning

Jesper E. van Engelen, Holger H. Hoos · Machine Learning · 2019 · artificial-intelligence · 10.1007/s10994-019-05855-6 · record engelen2019-a-survey-on-semi-supervised-learni
tag layer · index · concepts · authors · עברית
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This record’s own method is review, and the tags below follow from that. A review’s findings, quantities and statistics are things stated IN this paper, and some of them report results obtained elsewhere. No tag on this page names a different author. Measured across every review in this corpus, 81% of a review’s tags carry no citation at all — most of them are the review speaking. The corpus does not adjudicate the rest: read an unmarked tag as a claim this paper makes, not necessarily one it made first.
How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Other fields define these differently the same word, defined another way in another discipline
taxonomy 6 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 25 concept tags on this paper, all 25 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
cluster assumption smoothness low-density manifold local similarity measure taxonomy inductive transductive self-training self-learning sets margin intrinsically semi-supervised Transductive graph-based methods graph construction graph weighting pseudo-labelled data Semi-supervised learning wrapper methods unsupervised preprocessing methods co-training pseudo-labelled boosting methods inference two alternating steps of _training pseudo-labelling unlabelled data similarity review
Method, as described: survey of two decades of semi-supervised classification, organised by a new taxonomy and closed by a comparison of the published empirical evaluations

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relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field
unrecordable relationunhoused_relationsa relation the paper asserts that this vocabulary cannot express, with the reason

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1007/s10994-019-05855-6. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 3 · claim_type 1 · concept_edges 9 · concepts 25 · content_tags 9 · contribution 1 · controls 3 · limitations 5 · mechanisms 6 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 17 · transfers 1 · unhoused_relations 1

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 7 of 9 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studysemi-supervised classification - using unlabelled data to beat a learner trained on the labelled data alone - and the assumptions that have to hold for it to be possible at all
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object_of_studythe paper's words
research_questionwhat the field's methods assume, how those assumptions relate to one another, and when unlabelled data actually helps
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research_questionthe paper's words
contributiona taxonomy of semi-supervised classifiers, and the argument that the field's three named assumptions are one assumption under three definitions of 'similar'
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contributionthe paper's words
claim_typecritique
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claim_typethe paper's words
phenomenonunlabelled data carrying information about the label distribution - when it does, and the fact that nobody can tell in advance whether it does
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phenomenonthe paper's words
methodreview
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also in: review
methodthe paper's words
mechanismsTHE CATEGORY IS DECIDED BY A CHOICE THE PRACTITIONER USUALLY DOES NOT KNOW THEY ARE MAKING: `The concept of similarity we choose, often implicitly, dictates what constitutes a cluster`
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mechanisms[0]0.95the tagger's words
mechanismsand the field's three named assumptions differ only in what they mean by `close` - smoothness says near in input space, low-density says in the same dense region, manifold says on the same low-dimensional surface
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mechanisms[1]0.95the tagger's words
mechanismsso all three are instances of the cluster assumption, which is itself the necessary condition for the whole enterprise: that p(x) carries information about p(y|x)
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mechanisms[2]0.95the tagger's words
mechanismsA TAXONOMY PAPER THAT DIAGNOSES WHAT IS WRONG WITH TAXONOMIES: `Existing categorizations ... are typically relatively flat, thereby failing to capture similarities between different groups of methods. Furthermore, the categorizations are often fine-tuned towards existing work, making them less suited for the inclusion of new approaches.` Two named failure modes - too shallow to show kinship, and overfitted to the papers that existed when it was drawn.
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mechanisms[3]0.95the tagger's words
mechanismsAND THE PROPOSED REPAIR IS STATED AS A DESIGN GOAL, NOT A RESULT: `we propose a new way to represent the spectrum ... we ATTEMPT to group them in a clear, FUTURE-PROOF way`. `attempt` and `future-proof` in one sentence - the second is a property no survey can demonstrate about itself at publication.
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mechanisms[4]0.9the tagger's words
mechanismsTHE FIELD'S OWN NAME FOR KNOWINGLY USING THE WRONG WORD: `in a slight abuse of notation`, used twice in this paper for two different acts - once to call lists `sets`, once to reassign X and x to random variables. Mathematics has a standard phrase for declaring a deliberate terminological error and continuing, and this survey uses it in a footnote and in body text without further comment.
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mechanisms[5]0.9the tagger's words
limitationsTHE NECESSARY CONDITION CANNOT BE CHECKED. `if the data points ... cannot be meaningfully clustered, it is impossible for a semi-supervised learning method to improve on a supervised learning method` - and whether they can is exactly what cannot be established in advance
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limitations[0]0.95the tagger's words
limitationsstated twice over, at both levels: it is difficult to DEFINE the conditions under which an algorithm works, and `rarely straightforward to evaluate to what extent these conditions are satisfied`
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limitations[1]0.95the tagger's words
limitationsIT IS IMPOSSIBLE TO GUARANTEE THAT UNLABELLED DATA WILL NOT DEGRADE PERFORMANCE - the method can do worse than ignoring the extra data, and no test tells you beforehand
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limitations[2]0.95the tagger's words
limitationsand no method exists to choose the algorithm a priori, exactly as in supervised learning - so the practitioner picks the assumption and finds out afterwards
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limitations[3]0.95the tagger's words
limitationsTHE CENTRAL CLAIM ABOUT THE TAXONOMY IS UNFALSIFIABLE AT TIME OF WRITING: `allowing researchers and practitioners alike to gain insight into the way semi-supervised learning methods relate to each other`, built in a `future-proof way`. The paper's stated advantage over prior categorizations is that new methods will fit it - a claim only later work can test, and the same claim the superseded categorizations could have made.
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limitations[4]0.85the tagger's words
transfersthe matching problem - find a subset of edges sharing no vertices - is taken from graph theory and used to pair unlabelled samples in a semi-supervised learner
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transfers[0]0.95the tagger's words
controlsthe published empirical comparisons are surveyed rather than a single one cited - eight data sets in one study, several neural methods on two image problems in another
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eliminates: a field-level claim resting on the studies that happened to work; the answer reported is that NO ALGORITHM UNIFORMLY OUTPERFORMED THE OTHERS and relative performance varied with the amount of unlabelled data · outcome: failed
controls[0]0.95the tagger's words
controlswhere degradation was observed, the condition producing it is isolated - a mismatch between the classes in the labelled and the unlabelled data
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eliminates: degradation being treated as unexplained noise; here it has a named cause that a practitioner can check for · outcome: held
controls[1]0.95the tagger's words
controlswhether the conditions for a given method to work can be evaluated on a given dataset
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eliminates: nothing - it is 'rarely straightforward to evaluate to what extent these conditions are satisfied', which is why the necessary condition is not a test · outcome: failed
controls[2]0.95the tagger's words
conceptscluster assumption
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concepts[0]0.95the paper's words
conceptssmoothness
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also in: smoothness
concepts[1]0.95the paper's words
conceptslow-density
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also in: low-density
concepts[2]0.95the paper's words
conceptsmanifold
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also in: manifold
concepts[3]0.95the paper's words
conceptslocal similarity measure
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concepts[4]0.95the paper's words
conceptstaxonomy
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also in: taxonomy
concepts[5]0.95the paper's words
conceptsinductive
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about this quote: THE TERM IS NOT IN THE QUOTE - the quote's former and latter terms are inductive and transductive methods, named in the sentence just before it ('between inductive and transductive methods'). That sentence is not quoted because reference-list text is spliced into its citation.
also in: inductive
concepts[6]0.95the paper's words
conceptstransductive
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about this quote: THE TERM IS NOT IN THE QUOTE - the quote's former and latter terms are inductive and transductive methods, named in the sentence just before it ('between inductive and transductive methods'). That sentence is not quoted because reference-list text is spliced into its citation.
also in: transductive
concepts[7]0.95the paper's words
conceptsself-training
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also in: self-training
concepts[8]0.95the paper's words
conceptsself-learning
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also in: self-learning
concepts[9]0.9the paper's words
conceptssets
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also in: sets
concepts[10]0.9the paper's words
conceptsmargin
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also in: margin
concepts[11]1.0the paper's words
conceptsintrinsically semi-supervised
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concepts[12]1.0the paper's words
conceptsTransductive graph-based methods
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concepts[13]0.9the paper's words
conceptsgraph construction
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concepts[14]0.9the paper's words
conceptsgraph weighting
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concepts[15]0.9the paper's words
conceptspseudo-labelled data
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concepts[16]0.95the paper's words
conceptsSemi-supervised learning
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concepts[17]0.95the paper's words
conceptswrapper methods
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concepts[18]0.95the paper's words
conceptsunsupervised preprocessing methods
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concepts[19]0.95the paper's words
conceptsco-training
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also in: co-training
concepts[20]0.95the paper's words
conceptspseudo-labelled boosting methods
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concepts[21]0.95the paper's words
conceptsinference
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also in: inference
concepts[22]0.95the paper's words
conceptstwo alternating steps of _training
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concepts[23]0.95the paper's words
conceptspseudo-labelling
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concepts[24]0.95the paper's words
unhoused_relationsco-training → self-training
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The paper says co-training extends self-training to multiple classifiers. Extension is not is-a, part-of, same-as, distinct-from, contrasted-with, operationalised-as, causes, or renamed-across-fields; the concept-edge schema has no extends type.
unhoused_relations[0]0.95the tagger's words
senses`similar` - THE LOAD-BEARING WORD OF THE WHOLE FIELD AND IT IS A CHOICE. Smoothness, low-density and manifold are three definitions of it, and the survey's contribution is showing they are the same assumption differently parameterised. WHICH ONE YOU PICKED IS USUALLY IMPLICIT
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senses[0]0.95the tagger's words
sensesthe cluster assumption - AS RE-READ HERE: not one assumption among several but the genus of which the other three are species, and equivalent to the necessary condition that p(x) carries information about p(y|x)
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senses[1]0.95the tagger's words
senses`semi-supervised learning works` - AS THE SURVEY IS FORCED TO QUALIFY IT: it can help, it can do nothing, and it can DEGRADE performance, with no way to know which in advance. The literature's record of the third case is described as UNDER-REPORTED
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senses[2]0.95the tagger's words
senses`sets` - A DECLARED TYPE ERROR, KEPT ON PURPOSE: `the collections of data points referred to here are technically lists. However, following common usage, in this survey, we refer to them as ‘sets’ and, in a slight abuse of notation, apply standard set-theoretic concepts to them.` The authors know the word is wrong, say so in a footnote, and use it anyway because the field does.
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senses[3]0.95the tagger's words
senses`abuse of notation` IS USED TWICE IN THIS PAPER FOR TWO DIFFERENT ACTS. The second is symbol reuse: `we use X and x to denote random variables and their realizations, respectively, rather than data points` - the same symbols carry a new meaning from that point on.
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senses[4]0.95the tagger's words
senses`inductive` / `transductive` - the split is defined by WHAT THE METHOD RETURNS, not by how it works: `the former attempt to find a classification model, whereas the latter are solely concerned with obtaining label predictions for the given unlabelled data points`.
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senses[5]0.95the tagger's words
senses'margin' - ONE TERM, TWO DIFFERENT KINDS OF OBJECT, AND THE SURVEY RECORDS BOTH WITHOUT CHOOSING: 'the _margin_, which is defined as the distance between the decision boundary and the data points closest to it. The term is also commonly used to describe the area extruding from the decision boundary in which no data points lie.' A SCALAR AND A REGION. Not two readings of one quantity - two different types, so 'maximise the margin' and 'lies within the margin' are grammatical with different referents, and only one of them can be a number. This record already carries the survey's declared type error over 'sets', kept on purpose, and its two senses of 'abuse of notation'; this is the same survey's third type-level observation, and the only one it does not flag as its own.
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senses[6]1.0the tagger's words
senses`pseudo-labelled data` - A NAME WHOSE PREFIX CARRIES THE ONLY WARNING. `Quotation withheld` `pseudo-` is doing all the epistemic work: the data is treated exactly like labelled data by the algorithm, and only the name records that the labels are the model's own guesses. Drop the prefix in a citation and the caution is gone with it.
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senses[7]0.95the tagger's words
sensesSemi-supervised learning - a machine-learning branch that performs tasks using both labelled and unlabelled data
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senses[8]0.95the tagger's words
sensesWrapper methods - pseudo-label unlabelled data externally while the supervised learner remains unaware of label provenance
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senses[9]0.95the tagger's words
sensesUnsupervised preprocessing methods - use unlabelled data for features, clustering, or initial parameters but give the classifier only original labels
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senses[10]0.95the tagger's words
sensesIntrinsically semi-supervised - no intermediate step and no supervised base learner
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senses[11]0.95the tagger's words
sensesCo-training - self-training extended to multiple classifiers that learn from one another's most confident predictions
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senses[12]0.95the tagger's words
sensesPseudo-labelled boosting methods - sequential ensembles whose classifiers use labels plus earlier classifiers' confident unlabelled predictions
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senses[13]0.95the tagger's words
sensesInference - the final transductive graph-based phase, where the graph assigns labels to unlabelled data points
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senses[14]0.95the tagger's words
sensesTraining in wrapper methods - fitting classifiers on labelled and possibly prior pseudo-labelled data
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senses[15]0.95the tagger's words
sensesPseudo-labelling in wrapper methods - turning confident predictions for unlabelled objects into labels for the next iteration
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senses[16]0.95the tagger's words
claim_relationscritiques → the presentation of smoothness, low-density and manifold as separate assumptions, when they are one assumption under three definitions of similarity
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claim_relations[0]0.95the tagger's words
claim_relationscritiques → the semi-supervised literature's record of when its methods fail, whose prevalence is described as likely under-reported
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claim_relations[1]0.95the tagger's words
claim_relationssupports → Oliver et al.'s finding that degradation appeared only under a class mismatch between the labelled and unlabelled sets
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claim_relations[2]0.95the tagger's words
concept_edgessmoothness —is-a→ cluster assumption
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Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: 'the semi-supervised learning assumptions' = the smoothness, low-density and manifold assumptions of the previous sentence; is-a is loose.
concept_edges[0]0.95the tagger's words
concept_edgesmanifold —is-a→ cluster assumption
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Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span introduces the cluster assumption as an additional assumption beside manifolds; is-a is loose.
concept_edges[1]0.85the tagger's words
concept_edgesself-training —same-as→ self-learning
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: Given parenthetically and without preference, in a sentence that cites a survey whose own title uses a THIRD name - 'Self-labeled techniques for semi-supervised learning…'. Three names for one method inside one sentence, and the paper treats the multiplicity as unremarkable. · the relation stated: one method, two names, offered parenthetically and without preference
concept_edges[2]0.95the tagger's words
concept_edgesinductive —distinct-from→ transductive
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the relation itself is separately evidenced · strength of the relation: 0.95 · note: The top-level split of the taxonomy, and the differentiating property is the OUTPUT TYPE - a model versus a label vector. · the relation stated: the taxonomy's top-level split, and the differentiating property is the OUTPUT TYPE - a model versus a label vector
concept_edges[3]0.95the tagger's words
concept_edgesgraph construction —part-of→ Transductive graph-based methods
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: 'consist of' is in the containment cue list, so this edge is cue-tested. Weight 0.9 rather than 1.0 - 'GENERALLY consist of' is the survey hedging across the family it is describing.
concept_edges[4]0.9the tagger's words
concept_edgesgraph weighting —part-of→ Transductive graph-based methods
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: Second of the three steps, same sentence, same hedge.
concept_edges[5]0.9the tagger's words
concept_edgesinference —part-of→ Transductive graph-based methods
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: The survey says these methods generally have the three steps, so the paper-level hedge is 0.9.
concept_edges[6]0.95the tagger's words
concept_edgespseudo-labelling —part-of→ wrapper methods
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: The procedure usually has these two alternating steps; weight 0.9 preserves that hedge. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'The procedure ... pseudo-labelling step'; 'wrapper methods' is named only in the preceding text.
concept_edges[8]0.95the tagger's words
concept_edgesunsupervised preprocessing methods —distinct-from→ wrapper methods
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the relation itself is separately evidenced · strength of the relation: 1 · note: The paper explicitly contrasts what the supervised classifier receives under the two method families.
concept_edges[9]0.95the tagger's words
content_tagsthe concept of similarity chosen, often implicitly, dictates what counts as a cluster
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content_tags.findings[0]0.95the tagger's words
content_tagsTHREE NAMES, ONE ASSUMPTION: smoothness, low-density and manifold boil down to different definitions of the similarity between points
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content_tags.findings[1]0.95the tagger's words
content_tagsif the data cannot be meaningfully clustered, semi-supervised learning cannot improve on supervised learning
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content_tags.findings[2]0.95the tagger's words
content_tagsit is impossible to guarantee that adding unlabelled data will not degrade performance
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content_tags.findings[3]0.95the tagger's words
content_tagsno algorithm uniformly outperformed the others, and relative performance varied with the amount of unlabelled data
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content_tags.findings[4]0.95the tagger's words
content_tagswhere degradation was observed it had a named condition - a mismatch between the classes present in the labelled data and those in the unlabelled data
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content_tags.findings[5]0.95the tagger's words
content_tagsthat similar points tend to belong to the same group - and the survey argues the field's other assumptions are more specific instances of it
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content_tags.definitions[0]0.95the tagger's words
content_tagssimilarity
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also in: similarity
content_tags.statistics[0]0.95the tagger's words
content_tagsunlabelled data
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content_tags.variables[0]0.95the tagger's words

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