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Supervised learning: Matlab handwritten digit recognition using Adaptive Boosting Algorithm

Write a MATLAB function for feature extraction and classification of handwritten digits using MNIST data, and readers for the MNIST data. Adaboost M2 algorithm should be used. PM for more info

Habilidades: Aprendizaje automático, Mathlab y Mathematica

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Información del empleador:
( 6 comentarios ) durham, United States

ID de proyecto: #12017815

Adjudicado a:

sahilpanjwani

Hello. I possess Masters in Computer Science with a specialization in Machine Learning. I have worked extensively with Matlab, especially for image processing. I'm new to freelancer.com and am looking to build my portf Más

$30 USD en 3 días
(0 comentarios)
0.0

7 los freelancers están ofertando un promedio de $175 para este trabajo.

aminariaz

sir i can help u with this project i have done masters in computer engg with specilization in image processing and pattern recognition this is a dummy bid i can give u an exact estimate after we discuss the project hop Más

$166 USD en 3 días
(69 comentarios)
5.7
RSRCH

Hello, I am a PhD student studying on Artifical Intelligence and Neural Networks, Statistical and Adaptive Signal Processing and quite strong in MATLAB. I did some similar projects recently. Thanks RSRCH

$250 USD en 25 días
(24 comentarios)
5.1
$250 USD en 3 días
(31 comentarios)
4.8
DanielVizcaya91

Hello there, my name is Daniel and I would love to help you out with this project. I have worked before with the MNIST dataset before, in matlab, so I know I can deliver a high quality code for you. As you can check on Más

$199 USD en 3 días
(16 comentarios)
4.9
$180 USD en 5 días
(4 comentarios)
3.1
$250 USD en 3 días
(2 comentarios)
1.7
tudormth2008

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$250 USD en 1 día
(1 comentario)
0.0
Valuesolutions

Hello, how are you? I hope you have a bright day/evening from your side. I have read the details provided, but please contact me so that we can discuss more on the project. I believe I have the required skills in this Más

$147 USD en 3 días
(0 comentarios)
1.5