@article{Leiva-Murillo2013a,

title = {Multitask Remote Sensing Data Classification},

author = {Jose M Leiva-Murillo and Luis Gomez-Chova and Gustavo Camps-Valls},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6214595},

issn = {0196-2892},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Geoscience and Remote Sensing},

volume = {51},

number = {1},

pages = {151--161},

publisher = {IEEE},

keywords = {Aggregates, angular image features, Cloud screening, covariate shift, covariate shift (CS), cross information, data processing problems, data set bias, domain adaptation, geophysical image processing, Hilbert space pairwise predictor Euclidean distanc, image classification, image feature nonstationary behavior, Kernel, land mine detection, land-mine detection, learning (artificial intelligence), Machine learning, matrix decomposition, matrix regularization, MTL, multisource image classification, multispectral images, multitask learning, multitask learning (MTL), multitask remote sensing data classification, multitemporal classification, multitemporal image classification, radar data, regularization schemes, relational operators, Remote sensing, small sample set problem, spatial image features, Standards, support vector machine, support vector machine (SVM), Support vector machines, SVM, temporal image features, Training, urban monitoring},

pubstate = {published},

tppubtype = {article}

}

@article{Landa-Torres2012,

title = {Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search\textemdashExtreme Learning Machine Approach},

author = {Itziar Landa-Torres and Emilio G Ortiz-Garcia and Sancho Salcedo-Sanz and Mar\'{i}a J Segovia-Vargas and Sergio Gil-Lopez and Marta Miranda and Jose M Leiva-Murillo and Javier Del Ser},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6200298},

issn = {1932-4553},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Journal of Selected Topics in Signal Processing},

volume = {6},

number = {4},

pages = {388--398},

abstract = {The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.},

keywords = {Companies, Company internationalization, corporative strategy, diverse activity, Economics, Electronic mail, ensembles, exporting, exporting performance, external markets, extreme learning machine ensemble, extreme learning machines, feature selection method, grouping-based harmony search, hard process, harmony search (HS), hybrid algorithm, hybrid algorithms, hybrid grouping harmony search-extreme learning ma, hybrid soft computing, international company, international trade, internationalization procedure, internationalization success, learning (artificial intelligence), Machine learning, organizational structure, Signal processing algorithms, Spanish manufacturing company, Training, value chain},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cruz2010,

title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},

author = {Fernando Perez-Cruz and S R Kulkarni},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},

issn = {1070-9908},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Signal Processing Letters},

volume = {17},

number = {4},

pages = {355--358},

abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},

keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Leiva-Murillo2007,

title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},

author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},

issn = {1045-9227},

year  = {2007},

date = {2007-01-01},

journal = {IEEE Transactions on Neural Networks},

volume = {18},

number = {5},

pages = {1433--1441},

publisher = {IEEE},

abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},

keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},

pubstate = {published},

tppubtype = {article}

}