Support Vector Machines Based Mobile Robot Path Planning in an Unknown Environment

In this paper the use of support vector machines (SVM) for path planning has been investigated through a Player/Stage simulation for various case studies. SVMs are maximum margin classifiers that obtain a non-linear class boundary between the data sets. In order to apply SVM to the path planning problem, the entire obstacle course is divided in to two classes of data sets and a separating class boundary is obtained using SVM. This non-linear class boundary line determines the heading of the robot for a collision-free path. Complex obstacles and maps have been created in the simulation environment of Player/Stage. The effectiveness of SVM for path planning on unknown tracks has been studied and the results have been presented. For the classification of newly detected data points in the unknown environment, the k-nearest neighbors algorithm has been studied and implemented.

Download Full-text

Non-linear Classification of Massive Datasets with a Parallel Algorithm of Local Support Vector Machines

Advanced Computational Methods for Knowledge Engineering - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-17996-4_21 ◽

2015 ◽

pp. 231-241 ◽

Cited By ~ 10

Author(s):

Thanh-Nghi Do

Keyword(s):

Support Vector Machines ◽

Parallel Algorithm ◽

Support Vector ◽

Linear Classification ◽

Massive Datasets ◽

Vector Machines ◽

Local Support ◽

Non Linear

Download Full-text

Support Vector Machines on Large Data Sets: Simple Parallel Approaches

Studies in Classification, Data Analysis, and Knowledge Organization - Data Analysis, Machine Learning and Knowledge Discovery ◽

10.1007/978-3-319-01595-8_10 ◽

2013 ◽

pp. 87-95 ◽

Cited By ~ 5

Author(s):

Oliver Meyer ◽

Bernd Bischl ◽

Claus Weihs

Keyword(s):

Support Vector Machines ◽

Large Data ◽

Large Data Sets ◽

Support Vector ◽

Data Sets ◽

Vector Machines

Download Full-text

Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses

Neural Computation ◽

10.1162/neco_a_00837 ◽

2016 ◽

Vol 28 (6) ◽

pp. 1217-1247 ◽

Cited By ~ 9

Author(s):

Yunlong Feng ◽

Yuning Yang ◽

Xiaolin Huang ◽

Siamak Mehrkanoon ◽

Johan A. K. Suykens

Keyword(s):

Support Vector Machines ◽

Robust Statistics ◽

Real Data ◽

Constructive Proof ◽

Support Vector ◽

Data Sets ◽

Large Margin ◽

Hinge Loss ◽

Vector Machines ◽

Smooth Classification

This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses. Besides the robustness, we also show that the proposed RSCV is simultaneously smooth, which again benefits from using smooth classification losses. The idea of proposing RSVC comes from M-estimation in statistics since the proposed robust and smooth classification losses can be taken as one-sided cost functions in robust statistics. Its Fisher consistency property and generalization ability are also investigated. Besides the robustness and smoothness, another nice property of RSVC lies in the fact that its solution can be obtained by solving weighted squared hinge loss–based support vector machine problems iteratively. We further show that in each iteration, it is a quadratic programming problem in its dual space and can be solved by using state-of-the-art methods. We thus propose an iteratively reweighted type algorithm and provide a constructive proof of its convergence to a stationary point. Effectiveness of the proposed classifiers is verified on both artificial and real data sets.

Download Full-text

Non‐linear avo inversion using support vector machines

10.1190/1.1843305 ◽

2004 ◽

Cited By ~ 9

Author(s):

Heidi Anderson Kuzma ◽

James W. Rector

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

Avo Inversion ◽

Vector Machines ◽

Non Linear

Download Full-text

Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

2007 4th International Conference on Electrical and Electronics Engineering ◽

10.1109/iceee.2007.4344994 ◽

2007 ◽

Cited By ~ 2

Author(s):

Jair Cervantes ◽

Xiaoou Li ◽

Wen Yu ◽

Javier Bejarano

Keyword(s):

Support Vector Machines ◽

Large Data ◽

Large Data Sets ◽

Support Vector ◽

Data Sets ◽

Vector Machines ◽

Minimum Enclosing Ball

Download Full-text

Modelling Concrete Strength Using Support Vector Machines

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.438-439.170 ◽

2013 ◽

Vol 438-439 ◽

pp. 170-173 ◽

Cited By ~ 3

Author(s):

Hai Ying Yang ◽

Yi Feng Dong

Keyword(s):

Compressive Strength ◽

Support Vector Machines ◽

Concrete Strength ◽

Support Vector ◽

Generalized Regression Neural Network ◽

Data Sets ◽

Risk Minimization ◽

Minimization Principle ◽

Vector Machines ◽

Compressive Strength Of Concrete

Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

Download Full-text