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    Pruning Machine Learning Models by Sparse Representation

    , M.Sc. Thesis Sharif University of Technology Khorashadizadeh, Amir Ehsan (Author) ; Babaiezadeh, Massoud (Supervisor)
    Abstract
    In recent years, Machine Learning models have been developed in Signal Processing, Computer Vision and Neuroscience areas. There are two categories of Machine Learning models which are supervised and unsupervised learning models. Regression and classification problems are two popular problems examples of supervised learning models. From unsupervised learning problems, we can mention the clustering problem. Support Vector Regression (SVR), Decision Tree Regression and Bagging Ensemble Regression models are some important models of the regression problem. For classification problems, we can also mention to Support Vector Classification, Decision Tree Classification, and Bagging Ensemble... 

    Complex Activity Recognition by Means of an IMU-Based Wearable System for the Purpose of PD Patients’ Rehabilitation

    , M.Sc. Thesis Sharif University of Technology Tahvilian, Ehsan (Author) ; Behzadipour, Saeed (Supervisor) ; Ali Beiglou, Leila (Co-Supervisor)
    Abstract
    Parkinson's is a disease caused by a disorder in the central nervous system of the body. There is no definite cure for this disease, but one of the ways to prevent the progress of this disease is to use movement therapy. One of the goals of designing wearable systems consisting of inertial sensors is to make it possible to perform this movement therapy from a distance. The purpose of the present study and research is to use the approach of simple and complex activities in order to increase the accuracy in the detection of activities and also to solve the problems of the previous system, with the help of creating the ability to detect complex meaningful activities for Parkinson's patients. In... 

    Combination of multiple classifiers with fuzzy integral method for classifying the EEG signals in brain-computer interface

    , Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 157-161 ; 8190426249 (ISBN); 9788190426244 (ISBN) Shoaie, Z ; Esmaeeli, M ; Shouraki, S. B ; Sharif University of Technology
    2006
    Abstract
    In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The...