Sentence examples for decision forest from inspiring English sources

The comparative analysis of the proposed classifiers has demonstrated that the best results are achieved by a decision forest made of 30 trees.

We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier.

A Randomized Decision Forest (RDF) is introduced to achieve robust recognition on decomposed indoor objects with raw point data.

For adaptation and classification, Chen et al. [65] also proposed Transfer Neural Decision Forest (Transfer-NDF) for use in the TNT framework.

Inspired from deep neural decision forest [66] and random forest [42], Transfer-NDF uses neural networks as decision trees as to build a forest of neural decision trees.

For the baseline method, extraversion and leadership classification models were constructed using support vector machine, decision forest and ridge regression classifiers.

Matlab's treebagger algorithm is used for performing learning with decision forests with 100 trees.

However, decision trees tend to converge to nonglobal optima (global optimization is NP-hard), and by splitting data, tend to block modeling of feature interactions; this defect can be alleviated to some extent through the use of decision forests.

For leadership prediction, the peak results are given using MRMR with random decision forests using 30 and 40 features while Ridge regression achieves a good performance of 71.6% using only 10 features.

Hansen et al. applied k-nearest neighbor, support vector machines with a radial basis function, Gaussian processes, and random decision forests to build models on dragonX descriptors for this problem.

We compared three commonly used classifiers linear discriminant analysis (LDA) [42], random decision forests (RF) [43] (150 trees), and a SVM [37] (C-SVM, linear kernel with c=1) using three feature representations: PCA, one based on histogram of oriented gradients (HOG) [44, 45], and another based on local binary pattern (LBP) [46, 47] features.