A New Distance Measure for Model-Based Sequence Clustering (2008)
Garcia-Garcia, Dario, Parrado-Hernandez, Emilio
We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially...
Low cost estimation of $\sigma$ for SVM using local features (2008)
Gomez-Verdejo, Vanessa, Lazaro-Gredilla, Miguel, Parrado-Hernandez, Emilio
We investigate low cost methods to select the spread parameter in RBF kernels for Support Vector Machines. These methods try to gain information about the local structure of the dataset from the...
Text classification with a Primal SVM endowed with domain knowledge (2008)
Parrado-Hernandez, Emilio, Hardoon, David
In this paper we solve a document classification task by incorporating prior/domain knowledge onto the SVM. The algorithm consists in to learn a prior classifier in the primal space (words) from an...
Discovering Music Structure via Similarity Fusion (2007)
Arenas-Garcia, Jeronimo, Parrado-Hernandez, Emilio, Meng, Anders, Hansen, Lars Kai, Larsen, Jan
Automatic methods for music navigation and music recommendation exploit the structure in the music to carry out a meaningful exploration of the "song space''. To get a satisfactory performance from...
Exploiting the Prior in the PAC-Bayes Bound (2007)
Parrado-Hernandez, Emilio, Shawe-Taylor, John, Ambroladze, Amiran
This paper presents two SVM-like classification algorithms whose design criterion is to minimise the PAC-Bayes bound instead of to maximise the classification margin. A main goal of this work is to...
Tighter PAC-Bayes Bounds (2007)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers. The bound is based on learning a prior over the distribution of classifiers with a part...
The Interplay of Optimization and Machine Learning Research (2006)
Kristin P. Bennett, P. Bennett, Emilio Parrado-Hernandez
The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine...
Prior Support Vector Machines: minimum-bound vs. maximum-margin classifiers (2005)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
In this paper we introduce a new algorithm to train Support Vector Machines that aims at the minimisation of the PAC-Bayes bound on the error instead of at the traditional maximisation of the margin....
Prior Support Vector Machines: minimum-bound vs. maximum-margin classifiers (2005)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
In this paper we introduce a new algorithm to train Support Vector Machines that aims at the minimisation of the PAC-Bayes bound on the error instead of at the traditional maximisation of the margin....
Learning the Prior for the PAC-Bayes Bound (2005)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
This paper presents a bound on the performance of a Support Vector Machine obtained within the PAC-Bayes framework. The bound is computed by means of the estimation of a prior of the distribution of...
Complexity of Pattern Classes and Lipschitz Property (2005)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
Rademacher and Gaussian complexities are successfully used in learning theory for measuring the capacity of the class of functions to be learnt. One of the most important properties for these...
Rademacher analysis of infimum classifiers (2005)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
This paper addresses the problem of analysing the performance of classifiers obtained as the infimum of a set of k weak learners. The main result consists in a bound on the error of these classifiers...
Learning the prior for the PAC-Bayes bound (2004)
Ambroladze, Amiran, Parrado-Hernandez, Emilio, Shawe-Taylor, John
This paper presents a bound on the performance of a Support Vector Machine obtained within the PAC-Bayes framework. The bound is computed by means of the estimation of a prior of the distribution of...