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Learning To Grasp Unknown Objects Using Weighted Random Forest Algorithm From Selective Image And Point Cloud Feature, Md Shahriar Iqbal
Learning To Grasp Unknown Objects Using Weighted Random Forest Algorithm From Selective Image And Point Cloud Feature, Md Shahriar Iqbal
Electronic Theses and Dissertations
This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters …
Practical Implementations Of The Active Set Method For Support Vector Machine Training With Semi-Definite Kernels, Christopher Sentelle
Practical Implementations Of The Active Set Method For Support Vector Machine Training With Semi-Definite Kernels, Christopher Sentelle
Electronic Theses and Dissertations
The Support Vector Machine (SVM) is a popular binary classification model due to its superior generalization performance, relative ease-of-use, and applicability of kernel methods. SVM training entails solving an associated quadratic programming (QP) that presents significant challenges in terms of speed and memory constraints for very large datasets; therefore, research on numerical optimization techniques tailored to SVM training is vast. Slow training times are especially of concern when one considers that re-training is often necessary at several values of the models regularization parameter, C, as well as associated kernel parameters. The active set method is suitable for solving SVM problem …