ScholarGate
Asistenti

Krahasoni metodat

Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.

Isolation Forest×Analiza me Komponente Kryesore×Pylli i Rastësishëm×
FushaMësimi i makinësMësimi i makinësMësimi i makinës
FamiljaMachine learningMachine learningMachine learning
Viti i origjinës200820022001
KrijuesiLiu, F.T., Ting, K.M. & Zhou, Z.-H.Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)Breiman, L.
LlojiUnsupervised ensemble (random partitioning trees)Unsupervised dimensionality reductionEnsemble (bagging of decision trees)
Burimi themeluesLiu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗
Emërtime të tjeraIsolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detectionTemel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transformRastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Të lidhura534
PërmbledhjaIsolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.
ScholarGateSeti i të dhënave
  1. v1
  2. 1 Burimet
  3. PUBLISHED
  1. v1
  2. 1 Burimet
  3. PUBLISHED
  1. v1
  2. 2 Burimet
  3. PUBLISHED

Shko te kërkimi Shkarko diapozitivat

ScholarGateKrahasoni metodat: Isolation Forest · Principal Component Analysis · Random Forest. Marrë më 2026-06-19 nga https://scholargate.app/sq/compare