Rank: 345 based on 324 downloads (last 30 days) and 4 files submitted
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Oswaldo Ludwig

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Company/University
KU Leuven

Personal Profile:

Oswaldo Ludwig received the M.Sc. degree in electrical engineering from the Federal University of Bahia, Brazil, in 2004, when he began to teach courses in electrical engineering, such as artificial intelligence, digital control, and digital signal processing. In 2012 he received the PhD degree in electrical engineering from University of Coimbra, where he worked as an assistant professor from 2012 until 2013. Nowadays he is working as a senior researcher at the Department of Computer Science of the KU Leuven. His research interests are in machine learning with application on several fields, such as natural language processing, pedestrian and vehicle detection in the domain of intelligent vehicles, and biomedical data mining.

Professional Interests:
Artificial Intelligence, Machine Learning

 

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Files Posted by Oswaldo View all
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(last 30 days)
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11 Oct 2013 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig neural networks, machine learning, maximummargin princip..., svm, regularization, pattern recognition 50 10
  • 5.0
5.0 | 1 rating
11 Sep 2012 Feature selector based on genetic algorithms and information theory. The algorithm performs the combinatorial optimization by using Genetic Algorithms. Author: Oswaldo Ludwig feature selection, genetic algorithms, artificial intelligen..., information theory, mutual information, pattern recognition 53 9
  • 3.4
3.4 | 6 ratings
17 Mar 2011 HOG descriptor for Matlab Image descriptor based on Histogram of Oriented Gradients for gray-level images Author: Oswaldo Ludwig image descriptor, computer vision, artificial intelligen... 202 20
  • 4.30769
4.3 | 13 ratings
04 Nov 2010 MMGDX: a maximum-margin training method for neural networks Maximum-margin training method applicable to MLP in the context of binary classification. Author: Oswaldo Ludwig neural networks, machine intelligence, machine learning, artificial intelligen..., machine vision 19 1
Comments and Ratings by Oswaldo View all
Updated File Comments Rating
24 Sep 2014 Feature selector based on genetic algorithms and information theory. The algorithm performs the combinatorial optimization by using Genetic Algorithms. Author: Oswaldo Ludwig

Mahyar,

I'm sorry you aren't able to read/interpret the file description: "... The arguments are the desired number of selected features (feat_numb), a matrix X, in which each column is a feature vector example...".

05 May 2014 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig

Dear Alweshah,

The best approach is to tune the values of nneu and the punishing parameter C (which can be set in the line 16 of the code) by cross-validation.

28 Oct 2013 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig

Yes, you can download the paper (see Sections 2, 3, and 4): https://www.researchgate.net/publication/256662118_Eigenvalue_decay_a_new_method_for_neural_network_regularization

24 Jan 2013 HOG descriptor for Matlab Image descriptor based on Histogram of Oriented Gradients for gray-level images Author: Oswaldo Ludwig

Could you explain why it was adopted blockwise normalization? From the best of my knowledge, there is no mathematical foundation behind HOG descriptors, only good sense. This is my version, which was made available to be evaluated by the community. Go ahead, you should make the comparison with other algorithms in your case study (Science is not religion, feel free to doubt and inovate).

13 Dec 2012 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig

Mahesh,

Regarding the application of my codes in multi-class problems, the feature selector based on genetic algorithms and information theory can deal with multi-class problem by adopting the target data, y, as a line vector in which each class is represented by a natural number, such as 1 for the first class, 2 for the second... The SVNN can be upgraded to solve multi-class problem by adapting the framework described in "K. Crammer and Y. Singer. On the Algorithmic Implementation of Multi-class SVMs, JMLR, 2001". However, since I finished my PhD, I have no time to work on this research line.

Comments and Ratings on Oswaldo's Files View all
Updated File Comment by Comments Rating
09 Oct 2014 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig alnasser, mustafa

can i use it for classification

24 Sep 2014 Feature selector based on genetic algorithms and information theory. The algorithm performs the combinatorial optimization by using Genetic Algorithms. Author: Oswaldo Ludwig Ludwig, Oswaldo

Mahyar,

I'm sorry you aren't able to read/interpret the file description: "... The arguments are the desired number of selected features (feat_numb), a matrix X, in which each column is a feature vector example...".

24 Sep 2014 Feature selector based on genetic algorithms and information theory. The algorithm performs the combinatorial optimization by using Genetic Algorithms. Author: Oswaldo Ludwig mahyar

Dear Oswaldo
what is the meaning "15" in Hy=entropia2([y;zeros(1,C)],15)?
Moreover, the Y dimension is not matched with zeros(1,C). because the Dimension of C is equal with number of features whereas the dimension of y is equal the number of input pairs.
So, there is a mismatch dimension to vertcat!
How we can solve this problem?

08 Jun 2014 Feature selector based on genetic algorithms and information theory. The algorithm performs the combinatorial optimization by using Genetic Algorithms. Author: Oswaldo Ludwig Kaplan, Dmitry

Can you please explain the meaning of the resolucao=15. Why 15?

05 May 2014 Support Vector Neural Network (SVNN) A new training method for MLP neural networks based on the same principles of SVM. Author: Oswaldo Ludwig Ludwig, Oswaldo

Dear Alweshah,

The best approach is to tune the values of nneu and the punishing parameter C (which can be set in the line 16 of the code) by cross-validation.

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