@article{Read2014bb,

title = {Multi-Dimensional Classification with Super-Classes},

author = {Jesse Read and Concha Bielza and Pedro Larranaga},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},

issn = {1041-4347},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {26},

number = {7},

pages = {1720--1733},

publisher = {IEEE},

abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},

keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},

pubstate = {published},

tppubtype = {article}

}

@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{Salamanca2012b,

title = {Bayesian Equalization for LDPC Channel Decoding},

author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6129544},

issn = {1053-587X},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {60},

number = {5},

pages = {2672--2676},

abstract = {We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input of the channel decoder. Our approach takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate. However, this solution cannot be computed in linear time, because it depends on all the transmitted symbols. Hence, we also put forward an approximation for each symbol's posterior, using the expectation propagation algorithm, which is optimal from the Kullback-Leibler divergence viewpoint and yields an equalization with a complexity identical to the BCJR algorithm. We also use a graphical model representation of the full posterior, in which the proposed approximation can be readily understood. The proposed posterior estimates are more accurate than those computed using the ML estimate for the CSI. In order to illustrate this point, we measure the error rate at the output of a low-density parity-check decoder, which needs the exact posterior for each symbol to detect the incoming word and it is sensitive to a mismatch in those posterior estimates. For example, for QPSK modulation and a channel with three taps, we can expect gains over 0.5 dB with same computational complexity as the ML receiver.},

keywords = {Approximation methods, Bayes methods, Bayesian equalization, Bayesian estimation problem, Bayesian inference, Bayesian methods, BCJR (Bahl\textendashCocke\textendashJelinek\textendashRaviv) algorithm, BCJR algorithm, Channel Coding, channel decoding, channel equalization, channel equalization problem, Channel estimation, channel state information, CSI, Decoding, equalisers, Equalizers, expectation propagation, expectation propagation algorithm, fading channels, graphical model representation, intersymbol interference, Kullback-Leibler divergence, LDPC, LDPC coding, low-density parity-check decoder, Modulation, parity check codes, symbol posterior estimates, Training},

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}

}