Results 261 to 270 of about 17,286,028 (305)

Hybrid CNN and random forest model with late fusion for detection of autism spectrum disorder in Toddlers.

MethodsX
Nawghare P, Prasad J.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Rotation forest of random subspace models

Intelligent Decision Technologies, 2022
During the last decade, a variety of ensembles methods has been developed. All known and widely used methods of this category produce and combine different learners utilizing the same algorithm as the basic classifiers. In the present study, we use two well-known approaches, namely, Rotation Forest and Random Subspace, in order to increase the ...
Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, George A. Gravvanis, Michael N. Vrahatis   +4 more
openaire   +2 more sources

Evaluating random forest models for irace

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
Automatic algorithm configurators can greatly improve the performance of algorithms by effectively searching the parameter space. As algorithm configuration tasks can have large parameter spaces and the execution of candidate algorithm configurations is often very costly in terms of computation time, further improvements in the search techniques used ...
Leslie Pérez Cáceres, Bernd Bischl, Thomas Stützle   +2 more
openaire   +3 more sources

Random Forest Models To Predict Aqueous Solubility

Journal of Chemical Information and Modeling, 2006
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules.
David S. Palmer, Noel M. O'Boyle, Robert C. Glen, John B. O. Mitchell   +3 more
openaire   +4 more sources

Forest Fire Models on Configuration Random Graphs

Fundamenta Informaticae, 2016
We consider a random process of fire propagation over the links of the two configuration graphs with random node degrees. Node degrees follow either the power-law or the Poisson distribution. A comparative analysis of these graph models in terms of the number of nodes remaining after the fire was performed.
Marina Leri, Yuri Pavlov
openaire   +3 more sources

Using Random Forest To Model the Domain Applicability of Another Random Forest Model

Journal of Chemical Information and Modeling, 2013
In QSAR, a statistical model is generated from a training set of molecules (represented by chemical descriptors) and their biological activities. We will call this traditional type of QSAR model an "activity model". The activity model can be used to predict the activities of molecules not in the training set.
openaire   +3 more sources

Random Forests for mapping and analysis of microkinetics models

Computers & Chemical Engineering, 2018
Abstract We introduce the application of an ensemble learning method known as Random Forests to microkinetics modeling and the computationally efficient integration of microkinetics into reaction engineering models. First, we show how Random Forests can be used for mapping pre-computed microkinetics data.
Behnam Partopour, Randy C. Paffenroth, Anthony G. Dixon   +2 more
openaire   +1 more source

Language model adaptation using Random Forests

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
In this paper we investigate random forest based language model adaptation. Large amounts of out-of-domain data are used to grow the decision trees while very small amounts of in-domain data are used to prune them back, so that the structure of the trees are suitable for the desired domain while the probabilities in the tree nodes are reliably ...
Anoop Deoras, Frederick Jelinek, Yi Su
openaire   +2 more sources

Language Modeling Experiments with Random Forests

2005
L. Breiman recently introduced the concept of random forests (randomly constructed collection of decision trees) for classification. We have modified the method for regression and applied it to language modeling for speech recognition. Random forests achieve excellent results in both perplexity and error rate.
openaire   +2 more sources

The largest tree in certain models of random forests

Random Structures and Algorithms, 1998
Summary: We consider four families of forests on \(n\) vertices: labeled and unlabeled forests containing rooted and unrooted trees, respectively. A forest is chosen uniformly from one of the given four families. The limiting distribution of the size of its largest tree is then studied as \(n\to\infty\). Convergences to discrete distributions depending
openaire   +3 more sources