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Filtering by: Advisor Raich, Raviv Remove constraint Advisor: Raich, Raviv Decade 2010-2019 Remove constraint Decade: 2010-2019

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  • Weak-supervision Time-series Analysis

    Creator:
    You, Zeyu
    Abstract:
    Efficient time-series analysis can impact multiple application domains such as motif discovery in gene analysis or music data, extracting spectro-temporal patterns in acoustic scene analysis, or annotating and classifying electrical bio-signals (such as ECG, EEG, and EMG) for medical applications. Time-series analysis involves a variety of tasks. To predict future...
    Resource Type:
    Dissertation
    Full Text:
    12, 2019. Title: Weak-supervision Time-series Analysis Abstract approved: Raviv Raich E�cient

  • Multiple Instance Learning for Histopathological Image Classification

    Creator:
    Lai, Phung
    Abstract:
    In histopathological image analysis, image classification as well as pattern detection play a crucial role in the diagnosis and treatment process since the goal is to not only differentiate cancer types but also identify cancerous manifestations. Fully supervised learning strategies tend to address these problems using manually annotated cancerous regions...
    Resource Type:
    Capstone Project
    Full Text:
    Instance Learning for Histopathological Image Classification Abstract approved: Raviv Raich In

  • Weak-supervision : Probabilistic Models and Inference

    Creator:
    Pham, Anh
    Abstract:
    In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
    Resource Type:
    Dissertation
    Full Text:
    : Weak-supervision: Probabilistic Models and Inference Abstract approved: Raviv Raich In supervised

  • A statistical inference framework for finding recurring patterns in large data with applications to energy management

    Creator:
    You, Zeyu
    Abstract:
    We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. We first consider a simple problem of finding a single unknown pattern in...
    Resource Type:
    Masters Thesis
    Full Text:
    Management Abstract approved: Raviv Raich We consider the problem of finding unknown patterns that are

  • Novelty Detection Under the Multi-Instance Multi-Label Framework

    Creator:
    Lou, Qi
    Abstract:
    Novelty detection plays an important role in machine learning and signal processing. This project studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred to as multi-instance multi-label (MIML) learning. Contrary to the common assumption...
    Resource Type:
    Capstone Project
    Full Text:
    -Instance Multi-Label Framework Abstract approved: Raviv Raich Novelty detection plays an important role

  • On surrogate supervision multi-view learning

    Creator:
    Jin, Gaole
    Abstract:
    Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision...
    Resource Type:
    Masters Thesis
    Full Text:
    : On Surrogate Supervision Multi-view Learning Abstract approved: Raviv Raich Data can be

  • A probabilistic framework and algorithms for modeling and analyzing multi-instance data

    Creator:
    Behmardi, Behrouz
    Abstract:
    Multi-instance data, in which each object (e.g., a document) is a collection of instances (e.g., word), are widespread in machine learning, signal processing, computer vision, bioinformatic, music, and social sciences. Existing probabilistic models, e.g., latent Dirichlet allocation (LDA), probabilistic latent semantic indexing (pLSI), and discrete component analysis (DCA), have been...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: Raviv Raich Multi-instance data, in which each object (e.g., a document) is a

  • On Convex Dimensionality Reduction for Classification

    Creator:
    Tammineni, Tez Deepthi
    Abstract:
    Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non...
    Resource Type:
    Capstone Project
    Full Text:
    . Title: On Convex Dimensionality Reduction for Classification. Abstract approved: Raviv Raich

  • Probabilistic models for classification of bioacoustic data

    Creator:
    Lakshminarayanan, Balaji
    Abstract:
    Probabilistic models have been successfully applied for a wide variety of problems, such as but not limited to information retrieval, computer vision, bio-informatics and speech processing. Probabilistic models allow us to encode our assumptions about the data in an elegant fashion and enable us to perform machine learning tasks such...
    Resource Type:
    Masters Thesis
    Full Text:
    Bioacoustic Data. Abstract approved: Raviv Raich Probabilistic models have been successfully applied for a

  • Iterative reconstruction methods of CT images using a statistical framework

    Creator:
    Delgado, Diana
    Abstract:
    Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography (CT) is currently the most commonly used medical imaging technology because it is easy to use, detectors and scanners are constantly improving, and more importantly,...
    Resource Type:
    Masters Thesis
    Full Text:
    : ______________________________________________________ Raviv Raich Medical imaging technologies play a vital role in early diagnosis of disease