Results 221 to 230 of about 67,103 (261)
Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature
Advanced Science, EarlyView.A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo +14 more
wiley +1 more source
Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests. [PDF]
Int J Med InformChan LE +12 more
europepmc +1 more source
Advanced Science, EarlyView.Conventional single‐gradient freeze‐casting typically produces unidirectional porous architectures with limited transverse connectivity. The Sequential Hybridization by Infiltration and Freeze‐casting Technique (SHIFT) addresses this constraint by integrating secondary aligned structures within a preformed primary scaffold.
Kiho Sung, Sungchul Shin
wiley +1 more source
Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries
Advanced Science, EarlyView.A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park +4 more
wiley +1 more source
Quantifying salinity in calcareous soils through advanced spectroscopic models: A comparative study of random forests and regression techniques across diverse land use systems. [PDF]
PLoS OneTahmoures M +4 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), 2021In 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
Combinatorics, Probability and Computing, 1992
A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2.
Tomasz Luczak 0001, Boris G. Pittel
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A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2.
Tomasz Luczak 0001, Boris G. Pittel
openaire +2 more sources
Soft Computing, 2018
Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Carlos Javier Mantas +3 more
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Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Carlos Javier Mantas +3 more
openaire +1 more source
Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, 2016
Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data
Angshuman Paul, Dipti Prasad Mukherjee
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Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data
Angshuman Paul, Dipti Prasad Mukherjee
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Pattern Recognition, 2022
Abstract Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation.
Jiawang Bai +5 more
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Abstract Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation.
Jiawang Bai +5 more
openaire +1 more source