We describe in this work a Core Generator for Pattern Recognition tasks. This tool is able to generate, according to user requirements, the hardware description of a digital architecture, which implements a Support Vector Machine, one of the current state\u2013of\u2013the\u2013art algorithms for pattern recognition. The output of the Core Generator consists of a high\u2013level language hardware core description, suitable to be mapped on a reconfigurable device, like a Field Programmable Gate Array (FPGA). As an example of the use of our tool, we compare different solutions, by targeting several reconfigurable devices, and implement the recognition part of a machine vision system for automotive applications
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm capable...
This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
A pattern recognition system that can process a large amount of image data at high speed is required...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
In this paper, we propose a digital architecture for support vector machine (SVM) learning and discu...
This paper describes the design and implementation of a hardware-software embedded system for face r...
Classifying Microarray data, which are of high dimensional nature, requires high computational power...
We present here a hardware-friendly version of the support vector machine (SVM), which is useful to ...
We present here a hardware-friendly version of the support vector machine (SVM), which is useful to ...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm capable...
This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
A pattern recognition system that can process a large amount of image data at high speed is required...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
In this paper, we propose a digital architecture for support vector machine (SVM) learning and discu...
This paper describes the design and implementation of a hardware-software embedded system for face r...
Classifying Microarray data, which are of high dimensional nature, requires high computational power...
We present here a hardware-friendly version of the support vector machine (SVM), which is useful to ...
We present here a hardware-friendly version of the support vector machine (SVM), which is useful to ...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm capable...
This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their...