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    A Semi Supervised Approach to Three Dimensional Human Pose Estimation

    , M.Sc. Thesis Sharif University of Technology Pourdamghani, Nima (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    In this research, we introduce a semi-supervised manifold regularization framework for hu- man pose estimation. Here we aim the three major challenges in discriminative human pose estimation. We utilize the unlabeled data to reduce the need to labeled data and compen- sate for the complexities in the input space. We model the underlying manifold by a nearest neighbor graph. Due to depth ambiguity which is the main challenge in this problem, the true underlying manifold of the data bends and gets too close to itself is some areas which results in poor graph construction. To solve this problem, we argue that the optimal graph is a subgraph of the k-nearest neighbor graph and employ an... 

    A Semi-Supervised Algorithms for Clustering Microarray Data

    , M.Sc. Thesis Sharif University of Technology Eslamzadeh, Habibollah (Author) ; Mahdavi Amiri, Nezamoddin (Supervisor) ; Madadkar Sobhani, Armin (Supervisor)
    Abstract
    Microarray which is also known as Biochip is a flat substrate of glass with the size of 1 ×1 cm on which a numerous number of biosensors are placed in an array format. Microarray DNAs are used to measure expression level of thousands of genes. Repeating these experiments in different conditions can result in patterns of expression. After preparation, the florescent sample is hybridized with the sensors of microarray surface and fluoresce intensities of the spots are measured by a special camera called CCD. The obtained pictures are examined by a computer and the spot lights converted into numerical data by image processing algorithms. Putting these numbers into matrices of size m×n is... 

    MRI Semi-Supervised Segmentation

    , M.Sc. Thesis Sharif University of Technology Izadi, Azadeh (Author) ; Bagheri Shouraki, Saeed (Supervisor)
    Abstract
    Image segmentation is a technique which divides an image into significant parts. The accuracy of this technique plays an important role when it applies on medical images. Among various image segmentation methods, clustering methods have been extensively investigated and used. Since it is an unsupervised method, the existence of a small amount of side-information which is extracted from a specific application (in this case, medical image) could improve its accuracy. Using this side-information in clustering methods introduces a new generation of clustering approaches called semi-supervised clustering. This information usually has a format of pair-wise constraints and can be prepared easily... 

    A biologically plausible learning method for neurorobotic systems

    , Article 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 128-131 ; 9781424420735 (ISBN) Davoudi, H ; Vosoughi Vahdat, B ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
    2009
    Abstract
    This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series... 

    Semi-supervised metric learning using pairwise constraints

    , Article 21st International Joint Conference on Artificial Intelligence, IJCAI-09, Pasadena, CA, 11 July 2009 through 17 July 2009 ; 2009 , Pages 1217-1222 ; 10450823 (ISSN) ; 9781577354260 (ISBN) Soleymani Baghshah, M ; Bagheri Shouraki, S ; Sharif University of Technology
    Abstract
    Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The existing methods that can consider both positive (must-link) and negative (cannot-link) constraints find linear transformations or equivalently global Mahalanobis metrics. Additionally, they find metrics only according to the data points appearing in constraints (without considering other data...