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	Author	Gemma Sanchez; Josep Llados; Enric Marti
	Title	Segmentation and analysis of linial texture in plans			Type	Conference Article
	Year	1997	Publication	Intelligence Artificielle et Complexité.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
	Abstract	The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
	Address	Paris, France
	Corporate Author				Thesis
	Publisher		Place of Publication	Paris	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	AERFAI
	Notes	DAG;IAM;			Approved	no
	Call Number	IAM @ iam @ SLM1997			Serial	1649
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Colour Constancy Beyond the Classical Receptive Field			Type	Journal Article
	Year	2018	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	40	Issue	9	Pages	2081 - 2094
	Keywords
	Abstract	The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2018a			Serial	2990
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	Author	Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva
	Title	Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty			Type	Conference Article
	Year	2022	Publication	IEEE International Conference on Signal Processing and Communications	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples.
	Address	Bangalore; India; July 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	SPCOM
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ PMD2022			Serial	3796
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	Author	Sergio Escalera; Stephane Ayache; Jun Wan; Meysam Madadi; Umut Guçlu; Xavier Baro
	Title	Inpainting and Denoising Challenges			Type	Book Whole
	Year	2019	Publication	The Springer Series on Challenges in Machine Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ EAW2019			Serial	3398
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	Author	Fei Yang; Kai Wang; Joost Van de Weijer
	Title	ScrollNet: DynamicWeight Importance for Continual Learning			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops	Abbreviated Journal
	Volume		Issue		Pages	3345-3355
	Keywords
	Abstract	The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity in other parameters. The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e.g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e.g., regularization-based approaches). However, all these methods assume that the importance of weights for each task is unknown prior to data exposure. In this paper, we propose ScrollNet as a scrolling neural network for continual learning. ScrollNet can be seen as a dynamic network that assigns the ranking of weight importance for each task before data exposure, thus achieving a more favorable stability-plasticity tradeoff during sequential task learning by reassigning this ranking for different tasks. Additionally, we demonstrate that ScrollNet can be combined with various CL methods, including regularization-based and replay-based approaches. Experimental results on CIFAR100 and TinyImagenet datasets show the effectiveness of our proposed method.
	Address	Paris; France; October 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ WWW2023			Serial	3945
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	Author	Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal
	Title	Results of the use of Kahoot! gamification tool in a course of Chemistry			Type	Conference Article
	Year	2018	Publication	4th International Conference on Higher Education Advances	Abbreviated Journal
	Volume		Issue		Pages	1215-1222
	Keywords
	Abstract	The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
	Address	Valencia; June 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	HEAD
	Notes	MV; no proj			Approved	no
	Call Number	Admin @ si @ ABN2018			Serial	3246
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	Author	Cristina Palmero; Oleg V Komogortsev; Sergio Escalera; Sachin S Talathi
	Title	Multi-Rate Sensor Fusion for Unconstrained Near-Eye Gaze Estimation			Type	Conference Article
	Year	2023	Publication	Proceedings of the 2023 Symposium on Eye Tracking Research and Applications	Abbreviated Journal
	Volume		Issue		Pages	1-8
	Keywords
	Abstract	The power requirements of video-oculography systems can be prohibitive for high-speed operation on portable devices. Recently, low-power alternatives such as photosensors have been evaluated, providing gaze estimates at high frequency with a trade-off in accuracy and robustness. Potentially, an approach combining slow/high-fidelity and fast/low-fidelity sensors should be able to exploit their complementarity to track fast eye motion accurately and robustly. To foster research on this topic, we introduce OpenSFEDS, a near-eye gaze estimation dataset containing approximately 2M synthetic camera-photosensor image pairs sampled at 500 Hz under varied appearance and camera position. We also formulate the task of sensor fusion for gaze estimation, proposing a deep learning framework consisting in appearance-based encoding and temporal eye-state dynamics. We evaluate several single- and multi-rate fusion baselines on OpenSFEDS, achieving 8.7% error decrease when tracking fast eye movements with a multi-rate approach vs. a gaze forecasting approach operating with a low-speed sensor alone.
	Address	Tubingen; Germany; May 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ETRA
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ PKE2023			Serial	3923
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	Author	Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles
	Title	ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research			Type	Conference Article
	Year	2021	Publication	Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	3467-3476
	Keywords
	Abstract	The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.
	Address	Virtual; June 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ MJE2021			Serial	3560
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	Author	Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera
	Title	A Framework of Multi-Classifier Fusion for Human Action Recognition			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1260 - 1265
	Keywords
	Abstract	The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BHG2014			Serial	2446
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund
	Title	Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning			Type	Conference Article
	Year	2015	Publication	IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW)	Abbreviated Journal
	Volume		Issue		Pages	22-29
	Keywords
	Abstract	The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective. The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology.
	Address	Boston; EEUU; June 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2015			Serial	2655
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	Author	Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva
	Title	CCLM: Class-Conditional Label Noise Modelling			Type	Conference Article
	Year	2023	Publication	11th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	14062	Issue		Pages	3-14
	Keywords
	Abstract	The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness.
	Address	Alicante; Spain; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ TNM2023			Serial	3925
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	Author	Graham D. Finlayson; Javier Vazquez; Fufu Fang
	Title	The Discrete Cosine Maximum Ignorance Assumption			Type	Conference Article
	Year	2021	Publication	29th Color and Imaging Conference	Abbreviated Journal
	Volume		Issue		Pages	13-18
	Keywords
	Abstract	the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between wavelengths (many of the assumed reflectances are, in reality, not possible). In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely. Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets. Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set.
	Address	Virtual; November 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CIC
	Notes	CIC			Approved	no
	Call Number	FVF2021			Serial	3596
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	Author	Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich
	Title	Perception Based Representations for Computational Colour			Type	Conference Article
	Year	2011	Publication	3rd International Workshop on Computational Color Imaging	Abbreviated Journal
	Volume	6626	Issue		Pages	16-30
	Keywords	colour perception, induction, naming, psychophysical data, saliency, segmentation
	Abstract	The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space.
	Address	Milan, Italy
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication		Editor	Raimondo Schettini, Shoji Tominaga, Alain Trémeau
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-20403-6	Medium
	Area		Expedition		Conference	CCIW
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ VMB2011			Serial	1733
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach			Type	Journal Article
	Year	2009	Publication	International Journal of Electronic Commerce	Abbreviated Journal
	Volume	14	Issue	1	Pages	89-108
	Keywords
	Abstract	The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1086-4415	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2009b			Serial	1237
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Efficient pairwise classification using Local Cross Off strategy			Type	Conference Article
	Year	2012	Publication	25th Canadian Conference on Artificial Intelligence	Abbreviated Journal
	Volume	7310	Issue		Pages	25-36
	Keywords
	Abstract	The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
	Address	Toronto, Ontario
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-30352-4	Medium
	Area		Expedition		Conference	AI
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2012c			Serial	2044
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