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Author A. Martinez; Jordi Vitria edit  doi
openurl 
  Title Learning mixture models using a genetic version of the EM algorithm. Type Journal Article
  Year 2000 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 21 Issue (up) 8 Pages 759–769  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MVi2000 Serial 335  
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Author Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Learning to Learn: From Smarts Machines to Intelligent Machines Type Journal
  Year 2008 Publication Patter Recognition Letters Abbreviated Journal PRL  
  Volume 29 Issue (up) 8 Pages 1024–1032  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2008a Serial 950  
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Author Mario Rojas; David Masip; A. Todorov; Jordi Vitria edit  url
doi  openurl
  Title Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models Type Journal Article
  Year 2011 Publication PloS one Abbreviated Journal Plos  
  Volume 6 Issue (up) 8 Pages e23323  
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  Abstract JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions		  
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  Publisher Public Library of Science Place of Publication Editor  
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  Notes OR;MV Approved no  
  Call Number Admin @ si @ RMT2011 Serial 1883  
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Author Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva edit   pdf
url  doi
openurl 
  Title Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder Type Journal Article
  Year 2012 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 36 Issue (up) 8 Pages 591-600  
  Keywords Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles  
  Abstract We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.  
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  Notes OR; HuPBA; MILAB Approved no  
  Call Number Admin @ si @ ISE2012 Serial 2143  
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Author David Guillamet; Jordi Vitria edit  doi
openurl 
  Title Evaluation of distance metrics for recognition based on non-negative matrix factorization Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue (up) 9-10 Pages 1599 –1605  
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  Abstract IF: 0.809  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GuV2003b Serial 380  
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