Results 271 to 280 of about 3,398,718 (314)

Uncover This Tech Term: Random Forest. [PDF]

Korean J Radiol
Yuruk YY.
europepmc   +1 more source

MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi‐Scale Information and Metric Learning Framework for scDNAm Data

Advanced Science, EarlyView.
MethyAnno enables robust and interpretable annotation of single‐cell DNA methylation data by integrating multi‐scale epigenetic information, bidirectional cross‐attention, and prototype‐based metric learning. The framework resolves rare and novel cell types across datasets while revealing cell‐type‐specific epigenetic signatures associated with disease
Yuhang Jia, Siyu Li, Songming Tang, Keju Gu, Shengquan Chen   +4 more
wiley   +1 more source

A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms. [PDF]

Front Oncol
Ma X, Jiang Y, Lin N, Ye Z, Li H.
europepmc   +1 more source

Spatial intensity modelling of ISIS-related attacks using random forest regression: A GeoAI-based analysis of GTD data (2012-2019). [PDF]

PLoS One
Abewardhana AABDP, Lakmali RGN, Genovese PV.   +2 more
europepmc   +1 more source

Random forest modeling to identify key farm-to-fork factors influencing Campylobacter ecology in pastured poultry systems. [PDF]

Poult Sci
Kim M, Al Hakeem WG, Rothrock MJ.
europepmc   +1 more source

Predicting Groundwater Hydrochemical Facies in Three Dimensions with Random Forest Classification, USA. [PDF]

Ground Water
Stackelberg PE, Knierim KJ, Belitz K, Cravotta CA, McCleskey RB, Killian CD.   +5 more
europepmc   +1 more source

Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest. [PDF]

Materials (Basel)
Su W, Cheng Y, Wei L, Zhong G, Zhou L, Liu F, Xie K.   +6 more
europepmc   +1 more source

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On learning Random Forests for Random Forest-clustering

2020 25th International Conference on Pattern Recognition (ICPR), 2021
In this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context. In particular, we investigated the suitability of Gaussian Density Forests [1], Random Forests specifically designed for density ...
Bicego, M, Escolano, F
openaire   +2 more sources