MULTIMODAL CHOICE MODELING USING RANDOM FOREST DECISION TREES

Multimodal Object Recognition Using Random Clustering Trees

In this paper, we present an object recognition approach that in addition allows to discover intra-class modalities exhibiting highcorrelated visual information. Unlike to more conventional approaches based on computing multiple specialized classi ers, the proposed approach combines a single classi er, Boosted Random Ferns (BRFs), with probabilistic Latent Semantic Analysis (pLSA) in order to r...

Random Decision Trees

Ancestry assessment using random forest modeling.

A skeletal assessment of ancestry relies on morphoscopic traits and skeletal measurements. Using a sample of American Black (n = 38), American White (n = 39), and Southwest Hispanics (n = 72), the present study investigates whether these data provide similar biological information and combines both data types into a single classification using a random forest model (RFM). Our results indicate t...

Object-oriented mapping of urban trees using Random Forest classifiers

Since vegetation in urban areas delivers crucial ecological services as a support to human well-being and to the urban population in general, its monitoring is a major issue for urban planners. Mapping and monitoring the changes in urban green spaces are important tasks because of their functions such as the management of air, climate and water quality, the reduction of noise, the protection of...

LTS using decision forest of regression trees and neural networks

Letter-to-sound (LTS) rules play a vital role in building a speech synthesis system. In this paper, we apply various Machine Learning approaches like Classification and Regression Trees (CART), Decision Forest, forest of Artificial Neural Network (ANN) and Auto Associative Neural Networks (AANN) for LTS rules. We used these techniques mainly for Schwa deletion in Hindi. We empirically show that...