Performance detection of an embedded system using Boosting Algorithm

The boosting algorithm which we introduce is a representational ensemble classification methodology. It is well known that the Boosting algorithm can improve the accuracy of any given learning algorithm and train the strong classifiers efficiently. A specific embedded hardware is designed for target identification task. This paper also evaluates the performance and implementation issues for the Boosting classification on the embedded hardware. Considering the limited source and the characteristics of the embedded system, this paper proposed an optimal memory allocation method for system optimization which is combining the general software optimization methods. And the method we proposed can also be used for the other embedded system which is support the cache configuration. Some testing samples show the effectiveness of the proposed technique.

[1]  S Edelman,et al.  A model of visual recognition and categorization. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[3]  Yoshua Bengio,et al.  Boosting Neural Networks , 2000, Neural Computation.

[4]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[5]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[7]  Takeo Kanade,et al.  A statistical approach to 3d object detection applied to faces and cars , 2000 .

[8]  Mahmut T. Kandemir,et al.  Memory system optimization of embedded software , 2003, Proc. IEEE.

[9]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[10]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[12]  Robert E. Schapire,et al.  Using output codes to boost multiclass learning problems , 1997, ICML.