Results 141 to 150 of about 2,068,010 (305)
Random model trees: an effective and scalable regression method [PDF]
, 2010 We present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction.
Pfahringer, Bernhard
core
Random forests classification.
, 2016 Random forests classification.
Wei Li (7081) +4 more
core +1 more source
CoRRWe propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained
Binh Duc Vu +3 more
openaire +2 more sources
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
wiley +1 more source
The Astrophysical JournalInterplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections.
Hameedullah Farooki +7 more
doaj +1 more source
Improving Random Forests [PDF]
, 2004 Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise, does not overfit and offers possibilities for explanation and visualization of its output.
openaire +1 more source
Advanced Engineering Materials, EarlyView.This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
The Non-Market Value of Biodiversity Enhancement in New Zealand's Planted Forests [PDF]
, 2012 This study investigates the non-market value of biodiversity enhancement in New Zealand’s planted forests using the stated choice experiments (CE) approach. This study focuses on two issues. One issue is policy orientated where we estimate the non-market
Yao, Richard
core
Variance reduction in purely random forests
, 2012 International audienceRandom forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult.
Genuer, Robin
core +1 more source
On the asymptotics of random forests
Journal of Multivariate Analysis, 2016 The last decade has witnessed a growing interest in random forest models which are recognized to exhibit good practical performance, especially in high-dimensional settings. On the theoretical side, however, their predictive power remains largely unexplained, thereby creating a gap between theory and practice. The aim of this paper is twofold. Firstly,
openaire +3 more sources