The Strength of Weak Learnability
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Evaluation of the streams-C C-to-FPGA compiler: an applications perspective
FPGA '01 Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Intelligent Systems
Artificial Neural Network Implementation on a Fine-Grained FPGA
FPL '94 Proceedings of the 4th International Workshop on Field-Programmable Logic and Applications: Field-Programmable Logic, Architectures, Synthesis and Applications
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Automatic translation of software binaries onto FPGAs
Proceedings of the 41st annual Design Automation Conference
IEEE Transactions on Neural Networks
Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework
Journal of VLSI Signal Processing Systems
Implementation of Rotation Invariant Multi-View Face Detection on FPGA
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
EURASIP Journal on Advances in Signal Processing
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We propose a method and a tool for automatic generation of hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in terms of FPGA's slice, using different weak classifiers based on the general concept of hyperrectangle. The main novelty of our approach is that the tool allows the user to find automatically an appropriate tradeoff between classification performances and hardware implementation cost, and that the generated architecture is optimized for each training process. We present results obtained using Gaussian distributions and examples from UCI databases. Finally, we present an example of industrial application of real-time textured image segmentation.