134 research outputs found
Multiform Adaptive Robot Skill Learning from Humans
Object manipulation is a basic element in everyday human lives. Robotic
manipulation has progressed from maneuvering single-rigid-body objects with
firm grasping to maneuvering soft objects and handling contact-rich actions.
Meanwhile, technologies such as robot learning from demonstration have enabled
humans to intuitively train robots. This paper discusses a new level of robotic
learning-based manipulation. In contrast to the single form of learning from
demonstration, we propose a multiform learning approach that integrates
additional forms of skill acquisition, including adaptive learning from
definition and evaluation. Moreover, going beyond state-of-the-art technologies
of handling purely rigid or soft objects in a pseudo-static manner, our work
allows robots to learn to handle partly rigid partly soft objects with
time-critical skills and sophisticated contact control. Such capability of
robotic manipulation offers a variety of new possibilities in human-robot
interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC),
Tysons Corner, VA, October 11-1
A Novel Model of Working Set Selection for SMO Decomposition Methods
In the process of training Support Vector Machines (SVMs) by decomposition
methods, working set selection is an important technique, and some exciting
schemes were employed into this field. To improve working set selection, we
propose a new model for working set selection in sequential minimal
optimization (SMO) decomposition methods. In this model, it selects B as
working set without reselection. Some properties are given by simple proof, and
experiments demonstrate that the proposed method is in general faster than
existing methods.Comment: 8 pages, 12 figures, it was submitted to IEEE International
conference of Tools on Artificial Intelligenc
Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations
Typically, nonlinear Support Vector Machines (SVMs) produce significantly
higher classification quality when compared to linear ones but, at the same
time, their computational complexity is prohibitive for large-scale datasets:
this drawback is essentially related to the necessity to store and manipulate
large, dense and unstructured kernel matrices. Despite the fact that at the
core of training a SVM there is a \textit{simple} convex optimization problem,
the presence of kernel matrices is responsible for dramatic performance
reduction, making SVMs unworkably slow for large problems. Aiming to an
efficient solution of large-scale nonlinear SVM problems, we propose the use of
the \textit{Alternating Direction Method of Multipliers} coupled with
\textit{Hierarchically Semi-Separable} (HSS) kernel approximations. As shown in
this work, the detailed analysis of the interaction among their algorithmic
components unveils a particularly efficient framework and indeed, the presented
experimental results demonstrate a significant speed-up when compared to the
\textit{state-of-the-art} nonlinear SVM libraries (without significantly
affecting the classification accuracy)
Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data
The recent development of more sophisticated spectroscopic methods allows acquisition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic classification (supervised and unsupervised). In this work, a supervised classification through a partial least squares discriminant analysis (PLS-DA) is performed on the hy- perspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches
Klasifikasi Penerima Program Beras Miskin (Raskin) Di Kabupaten Wonosobo Dengan Metode Support Vector Machine Menggunakan Libsvm
Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF)
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