Automatic Car Parking: A Reinforcement Learning Approach

Kernel based learning has found wide applications in several data mining problems. In this paper, we propose a modified classical linear kernel using an automatic smoothing parameter (Sp) selection compared with the existing approach. We designed the Sp values using the Eigen values computed from the dataset. Experiment results using some classification related benchmark datasets reveal that the improved linear kernel method performed better than some of the existing kernel techniques.

[1]  Javier de Lope,et al.  A bio-inspired robotic mechanism for autonomous locomotion in unconventional environments , 2003 .

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[6]  Changjiu Zhou,et al.  Dynamic balance of a biped robot using fuzzy reinforcement learning agents , 2003, Fuzzy Sets Syst..

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  Ljubo Vlacic,et al.  10 – Fuzzy control , 2001 .

[9]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[10]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[11]  Bart Kosko,et al.  Comparison of fuzzy and neural truck backer-upper control systems , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[12]  David J. Crisp,et al.  Uniqueness of the SVM Solution , 1999, NIPS.

[13]  Carlos Canudas de Wit TRENDS IN MOBILE ROBOT AND VEHICLE CONTROL , 1998 .

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  Ajith Abraham,et al.  An Empirical Comparison of Kernel Selection for Support Vector Machines , 2002, HIS.

[16]  Christian Laugier,et al.  Motion generation and control for parking an autonomous vehicle , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[17]  I. E. Paromtchik,et al.  Sensor-Based Control Architecture for a Car-Like Vehicle , 1999, Auton. Robots.

[18]  Richard M. Murray,et al.  A motion planner for nonholonomic mobile robots , 1994, IEEE Trans. Robotics Autom..

[19]  Gunnar Rätsch,et al.  Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites , 2000, German Conference on Bioinformatics.

[20]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[21]  Javier de Lope,et al.  A REINFORCEMENT LEARNING METHOD FOR DYNAMIC OBSTACLE AVOIDANCE IN ROBOTIC MECHANISMS , 2002 .

[22]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[23]  Dongbing Gu,et al.  Neural predictive control for a car-like mobile robot , 2002, Robotics Auton. Syst..