A Random Graph is a random object which takes its values in a space of graphs. We take advantage of the expressibility of graphs in order to model uncertainty about the existence of causal relations within a given set of variables. We... more
A Random Graph is a random object which takes its values in a space of graphs. We take advantage of the expressibility of graphs in order to model uncertainty about the existence of causal relations within a given set of variables. We adopt a Bayesian point of view which leads us to propose a belief updating procedure with the objective of capturing a causal structure via interaction with a causal environment. Besides learning a causal structure, our proposal is also able to learn optimal actions, i.e., an optimal policy. We test our method in two experiments, each on a different scenario. Our experiments confirm that the proposed technique is able to learn a causal structure as well as an optimal policy. On the other hand, the second experiment shows that our proposal manages to learn an underlying causal model within several tasks in which different configurations of the causal structure are used.
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sampled from the model which characterizes... more
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sampled from the model which characterizes the distribution of such solutions. The main framework of the proposed method is an estimation of distribution algorithm, in which an adaptive Gibbs sampling is used to generate new promising solutions and, in combination with a local search strategy, it improves the individual solutions produced in each iteration. The Estimation of Distribution Algorithm with Adaptive Gibbs Sampling we are proposing in this paper is called AGEDA. We experimentally evaluate and compare this algorithm against two deterministic procedures and several stochastic methods in three well known test problems for unconstrained global optimization. It is empirically shown that our heuristic is robust in problems that involve three central aspects that mainly determine the difficulty of global optimization problems, namely high-dimensionality, multi-modality and non-smoothness.
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In this paper we report results of experiments conducted with strategies for improving text-based image retrieval. The adopted strategies were evaluated in the photographic retrieval task at ImageCLEF2007. We propose a Web-based method... more
In this paper we report results of experiments conducted with strategies for improving text-based image retrieval. The adopted strategies were evaluated in the photographic retrieval task at ImageCLEF2007. We propose a Web-based method for expanding textual queries with related terms. This technique was the top-ranked query expansion method among those proposed by other ImageCLEF2007 participants. We also consider two methods for combining visual and textual information in the retrieval process: late-fusion and intermedia-feedback. The best results were obtained by combining intermedia-feedback and our expansion technique. The main contribution of this paper, however, is the proposal of ”annotation-based expansion”; a novel approach that consists of using labels assigned to images (with image annotation methods) for expanding textual queries and documents. We introduce this idea and report results of initial experiments towards enhancing text-based image retrieval via image annotation. Preliminary results show that this expansion strategy could be useful for image retrieval in the near future.
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Abstract. In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of... more
Abstract. In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of stellar populations parameters as well as on machine learning benchmark data, inserting artificial noise and outliers. We used kernel principal component analysis in order to reduce the dimensionality of the spectral data. Experiments on noisy and noiseless data were performed. Keywords: Outlier ...
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Abstract. In this paper a new approach to noise detection and elimination in datasets for machine learning is presented. An algorithm that improves quality in training sets is introduced. This algorithm is based in the re-sampling idea... more
Abstract. In this paper a new approach to noise detection and elimination in datasets for machine learning is presented. An algorithm that improves quality in training sets is introduced. This algorithm is based in the re-sampling idea that allows improving training data quality by identifying possible noisy instances and performing new measurements of each selected instance.
Accuracy of current automatic image labeling methods is under the requirements of annotation-based image retrieval systems. The performance of most of these labeling methods is poor if we just consider the most relevant label for a given... more
Accuracy of current automatic image labeling methods is under the requirements of annotation-based image retrieval systems. The performance of most of these labeling methods is poor if we just consider the most relevant label for a given region. However, if we look within the set of the top− k candidate labels for a given region, accuracy of most of these systems is improved.
Abstract. A kernel-based algorithm for useful-anomaly detection and noise elimination is introduced. The algorithm's objective is to improve data quality by correcting wrong observations while leaving intact the correct ones. The proposed... more
Abstract. A kernel-based algorithm for useful-anomaly detection and noise elimination is introduced. The algorithm's objective is to improve data quality by correcting wrong observations while leaving intact the correct ones. The proposed algorithm is based on a process that we called” Re-Measurement” and it is oriented to datasets that might contain both kinds of rare objects: noise and usefulanomalies. Two versions of the algorithm are presented R− V 1 and R− V 2.
Abstract. In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of... more
Abstract. In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of stellar populations parameters as well as on machine learning benchmark data, inserting artificial noise and outliers. We used kernel principal component analysis in order to reduce the dimensionality of the spectral data. Experiments on noisy and noiseless data were performed.
Las áreas de visión y aprendizaje computacionales han interactuado notablemente desde los inicios de la computación. Prueba de ello son los experimentos realizados por Frank Rosenblatt entre 1957 y 1962, quien programó el algoritmo... more
Las áreas de visión y aprendizaje computacionales han interactuado notablemente desde los inicios de la computación. Prueba de ello son los experimentos realizados por Frank Rosenblatt entre 1957 y 1962, quien programó el algoritmo Perceptron en la computadora Mark 1 [1]. La entrada para el Perceptron eran imágenes digitales muy primitivas y éste tenıa que aprender a reconocer correctamente caracteres en ellas.
Abstract We organized a challenge on gesture recognition: http://gesture. chalearn. org. We made available a large database of 50,000 hand and arm gestures videorecorded with a Kinect™ camera providing both RGB and depth images. We used... more
Abstract We organized a challenge on gesture recognition: http://gesture. chalearn. org. We made available a large database of 50,000 hand and arm gestures videorecorded with a Kinect™ camera providing both RGB and depth images. We used the Kaggle platform to automate submissions and entry evaluation. The focus of the challenge is on “one-shot-learning”, which means training gesture classifiers from a single video clip example of each gesture.
We present methods for image annotation and retrieval based on semantic cohesion among terms. On the one hand, we propose a region labeling technique that assigns an image the label that maximizes an estimate of semantic cohesion among... more
We present methods for image annotation and retrieval based on semantic cohesion among terms. On the one hand, we propose a region labeling technique that assigns an image the label that maximizes an estimate of semantic cohesion among candidate labels associated to regions in segmented images. On the other hand, we propose document representation techniques based on semantic cohesion among multimodal terms that compose images.
Abstract. We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations... more
Abstract. We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations by using kernel methods. We apply our noise-aware algorithms to the prediction of stellar population parameters, a challenging astronomical problem.
Abstract. 1We present methods for image annotation and retrieval based on semantic cohesion among terms. On the one hand, we propose a region labeling technique that assigns an image the label that maximizes an estimate of semantic... more
Abstract. 1We present methods for image annotation and retrieval based on semantic cohesion among terms. On the one hand, we propose a region labeling technique that assigns an image the label that maximizes an estimate of semantic cohesion among candidate labels associated to regions in segmented images. On the other hand, we propose document representation techniques based on semantic cohesion among multimodal terms that compose images.
Abstract Artificial neural networks have been proven to be effective learning algorithms since their introduction. These methods have been widely used in many domains, including scientific, medical, and commercial applications with great... more
Abstract Artificial neural networks have been proven to be effective learning algorithms since their introduction. These methods have been widely used in many domains, including scientific, medical, and commercial applications with great success. However, selecting the optimal combination of preprocessing methods and hyperparameters for a given data set is still a challenge. Recently a method for supervised learning model selection has been proposed: Particle Swarm Model Selection (PSMS).
Abstract This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as... more
Abstract This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations.
Abstract—One of the main problems in any machine learning experiment is to tackle overfitting. Traditionally, this problem is approached by tuning the parameters related to the complexity of the technique. This is generally done... more
Abstract—One of the main problems in any machine learning experiment is to tackle overfitting. Traditionally, this problem is approached by tuning the parameters related to the complexity of the technique. This is generally done performing a crossvalidation on the training set or using information criteria (eg, Akaike information criterion). In this contribution, we treat the problem of overfitting from a different perspective.
Resumen En este trabajo se presenta la comparación de dos modelos formulados para la solución de un problema de diseño de territorios comerciales donde se desea minimizar la dispersión territorial. Este trabajo es motivado por un caso... more
Resumen En este trabajo se presenta la comparación de dos modelos formulados para la solución de un problema de diseño de territorios comerciales donde se desea minimizar la dispersión territorial. Este trabajo es motivado por un caso práctico de una compañía distribuidora de bebidas embotelladas. El primer modelo utiliza como medida de dispersión la métrica del problema de localización del p-centro, y el segundo la métrica del problema de la p-mediana.
This paper introduces a new similarity measure called weighted profile intersection (WPI) for profile-based authorship attribution (PBAA). Authorship attribution (AA) is the task of determining which, from a set of candidate authors,... more
This paper introduces a new similarity measure called weighted profile intersection (WPI) for profile-based authorship attribution (PBAA). Authorship attribution (AA) is the task of determining which, from a set of candidate authors, wrote a given document. Under PBAA an author's profile is created by combining information extracted from sample documents written by the author of interest. An unseen document is associated with the author whose profile is most similar to the document.
Abstract In this paper we propose an energy-based model (EBM) for selecting subsets of features that are both causally and predictively relevant for classification tasks. The proposed method is tested in the causality challenge, a... more
Abstract In this paper we propose an energy-based model (EBM) for selecting subsets of features that are both causally and predictively relevant for classification tasks. The proposed method is tested in the causality challenge, a competition that promotes research on strengthen feature selection by taking into account causal information of features.
The paper proposes the use of the Silhouette Coefficient (SC) as a ranking measure to perform instance selection in text classification. Our selection criterion was to keep instances with mid-range SC values while removing the instances... more
The paper proposes the use of the Silhouette Coefficient (SC) as a ranking measure to perform instance selection in text classification. Our selection criterion was to keep instances with mid-range SC values while removing the instances with high and low SC values. We evaluated our hypothesis across three well-known datasets and various machine learning algorithms. The results show that our method helps to achieve the best trade-off between classification accuracy and training time.
Abstract This paper describes a web-based interactive approach for building visual vocabularies in support of image annotation and retrieval. Under the proposed formulation a semantic concept is associated with a specific visual... more
Abstract This paper describes a web-based interactive approach for building visual vocabularies in support of image annotation and retrieval. Under the proposed formulation a semantic concept is associated with a specific visual representation, thereby, explicitly modeling the 'semantic gap'. Visual concepts obtained in this way can be used for image annotation/retrieval, object recognition and detection for both images and video and for building visual resources like visual dictionaries.
Abstract In this paper we propose the application of particle swarm optimization (PSO) to the problem of full model selection (FMS) for classification tasks. We define FMS as follows: given a pool of preprocessing methods, feature... more
Abstract In this paper we propose the application of particle swarm optimization (PSO) to the problem of full model selection (FMS) for classification tasks. We define FMS as follows: given a pool of preprocessing methods, feature selection and learning algorithms, select the combination of these that obtains the lowest classification error for a given data set; the task also includes the selection of hyperparameters for the considered methods.
Abstract In this paper, three approaches to utilizing object-level spatial contextual information for semantic image analysis are presented and comparatively evaluated. Contextual information is in the form of fuzzy directional relations... more
Abstract In this paper, three approaches to utilizing object-level spatial contextual information for semantic image analysis are presented and comparatively evaluated. Contextual information is in the form of fuzzy directional relations between image regions.
Abstract This paper describes the system jointly developed by the Language Technologies Lab from INAOE and the Language and Reasoning Group from UAM for the Sexual Predators Identification task at the PAN 2012. The presented system... more
Abstract This paper describes the system jointly developed by the Language Technologies Lab from INAOE and the Language and Reasoning Group from UAM for the Sexual Predators Identification task at the PAN 2012. The presented system focuses on the problem of identifying sexual predators in a set of suspicious chatting.
Abstract. This paper describes experimental results of two approaches to multimedia image retrieval: annotation-based expansion and late fusion of mixed methods. The former formulation consists of expanding manual annotations with labels... more
Abstract. This paper describes experimental results of two approaches to multimedia image retrieval: annotation-based expansion and late fusion of mixed methods. The former formulation consists of expanding manual annotations with labels generated by automatic annotation methods. Experimental results show that the performance of text-based methods can be improved with this strategy, specially, for visual topics; motivating further research in several directions.
Abstract This paper describes the participation of the INAOE's research group on machine learning for image processing and information retrieval from México. This year we proposed two approaches for the photographic retrieval task. First,... more
Abstract This paper describes the participation of the INAOE's research group on machine learning for image processing and information retrieval from México. This year we proposed two approaches for the photographic retrieval task. First, we studied the annotation-based expansion of documents for image retrieval. This approach consists of automatically assigning labels to images by using supervised machine learning techniques. Labels are used for expanding the manual annotations of images.
Abstract This paper introduces a Quadtree image segmentation technique to be used for image annotation. The proposed method is able to efficiently divide the image in homogeneous segments by merging adjacent regions using border and color... more
Abstract This paper introduces a Quadtree image segmentation technique to be used for image annotation. The proposed method is able to efficiently divide the image in homogeneous segments by merging adjacent regions using border and color information. Our method is highly efficient and provides segmentations of acceptable performance; the segments generated with the proposed technique can be used for automatic image annotation and related tasks (eg, object recognition).
Abstract We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations by... more
Abstract We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations by using kernel methods. We apply our noiseaware algorithms to the prediction of stellar population parameters, a challenging astronomical problem.
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This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very... more
This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a mid-level description of the image (i.e., in terms of the
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This paper introduces an energy-based model (EBM) for region labeling that takes advantage of both context and semantics present in segmented images.The proposed method refines the output of multiclass classification methods that are... more
This paper introduces an energy-based model (EBM) for region labeling that takes advantage of both context and semantics present in segmented images.The proposed method refines the output of multiclass classification methods that are based on the one-vs-all (OVA) formulation. Intuitively, the EBM maximizes the semantic cohesion among labels assigned to neighboring regions; that is, a tradeoff between label-association information and