Semi-supervised Logistic Regression (Self-training and EM-based variants)
Fikiria kufundisha kiashiria cha ugonjwa na utambuzi 50 tu uliothibitishwa lakini rekodi 5,000 za wagonjwa ambapo matokeo hayajulikani. Usilimiaji wa kawaida wa mantiki hupuuza kabisa rekodi hizo zisizo na lebo. Usilimiaji wa usaidizi wa mantiki hutumia utabiri wa uwezekano wa modeli yenyewe kupeana lebo za majaribio kwa visa visivyo na lebo, hufunzwa tena kwa seti iliyopanuliwa ya lebo bandia, na hurudia. Kila mzunguko husogeza mpaka wa uamuzi kuelekea maeneo yenye msongamano mwingi wa data, kwa ufanisi kuruhusu data isiyo na lebo kuongoza mahali ambapo mpaka unapaswa kuwa bila kuhitaji utambuzi wa gharama kubwa wa kitaalamu.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI: 10.1023/a:1007692713085 ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Semi-supervised Logistic Regression (Self-training and EM-based variants). ScholarGate. https://scholargate.app/sw/machine-learning/semi-supervised-logistic-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Uenezaji wa LeboUjifunzaji wa Mashine↔ compare
- Regressioni ya Lojistiki (ML)Ujifunzaji wa Mashine↔ compare
- Usalama-wa-kujitegemea wa Usawazishaji wa UsawaUjifunzaji wa Mashine↔ compare
- Ujifunzaji Nusu-SimamiwaUjifunzaji wa Mashine↔ compare
- Naive Bayes Semi-iliyojumuUjifunzaji wa Mashine↔ compare
Imerejelewa na
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