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https://www.jopdesign.com/doc/rtsvm_wises2006.pdf
AN EMBEDDED SUPPORT VECTOR MACHINE cache the matrix in its full n×n. size. The SVM follows a three stage approach like many other machine learning algorithms: Train, test, and predict. We focus on the kernel function which is common for all three states. Our focus is to test whether we can implement a faster kernel function in HW (VHDL) than in SW (Java).
https://www.researchgate.net/publication/224063206_An_Embedded_Support_Vector_Machine
The support vector machine has been chosen because it represents a framework both inter - esting from a machine learning perspective and from an embedded systems perspectiv e. A SVM …
https://www.sciencedirect.com/science/article/abs/pii/S187705091300330X
The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting ...
http://cas.ee.ic.ac.uk/people/ccb98/papers/Embedded%20Hardware-Efficient%20Real-Time%20Classification%20with%20Cascade%20Support%20Vector%20Machines.pdf
A Support Vector Machine (SVM) is a supervised binary classification algorithm which maps data into a high- dimensional space where an optimal separating hyperplane is constructed.
https://www.semanticscholar.org/paper/Support-Vector-Machines-with-Embedded-Reject-Option-Fumera-Roli/e0c2fe842151d108f7b78aaffba46d215be9d812
In this paper, the problem of implementing the reject option in support vector machines (SVMs) is addressed. We started by observing that methods proposed so far simply apply a reject threshold to the outputs of a trained SVM. We then showed that, under the framework of the structural risk minimisation principle, the rejection region must be determined during the training phase of a classifier ...
https://digilander.libero.it/sedekfx/papers_/ijcnn2006.pdf
Abstract — Training Support Vector Machines (SVMs) requires efficient architectures, endowed with agile memory handling and specific computational features. Such a process is often supported by embedded implementations on dedicated machinery, for …
https://www.cs.colorado.edu/~jbg/teaching/CSCI_5622/09b.pdf
Kernel Functions for Support Vector Machines Jordan Boyd-Graber University of Colorado Boulder LECTURE 9B Slides adapted from Jerry Zhu Jordan Boyd-Graber j Boulder Kernel Functions for Support Vector Machines j 1 of 13
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM …
https://en.wikipedia.org/wiki/Feature_selection
One other popular approach is the Recursive Feature Elimination algorithm, commonly used with Support Vector Machines to repeatedly construct a model and remove features with low weights. Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process.
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