Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"parameter learning"”.
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Parameter learning with particle filters
… models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this problem is to recalibrate at times …
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Fast Parameter Learning in Adaptive Systems
… tracking. A central part of adaptive control is parameter estimation. Real-time adaptive control requires parameter estimation to occur quickly and accurately. This thesis is dedicated to fast parameter estimation in adaptive identification and control of linear dynamic systems with …
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An analysis of finite parameter learning in linguistic spaces
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models
… Because the training data is so sparse, a learning algorithm that builds predictive models must be able to exploit the availability of such rich domain knowledge. However such medical knowledge is rarely employed when using machine learning for building predictive models. Such knowledge has …
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Weighted geometric grammars for object detection in context
… We adapt the structured perceptron algorithm to parameter learning in WGG models, and develop a set of original clustering-based algorithms for structure learning. We then demonstrate empirically that WGG models, with parameters and structure learned automatically from data, can outperform a …
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Crises Learning Under Diagnosticity
… posterior inference and interferes with Bayesian parameter learning. Conversely, we study scenarios where parameter uncertainty dampens or magnifies extrapolation. We characterise two unique implications of such an interaction with supporting evidence from the SPF: 1) State-dependence of …
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Energy-efficient information inference in wireless sensor networks based on graphical modeling
… MRF model is first constructed through automatic learning from historical sensed data, by using Iterative Proportional Fitting (IPF). When the MRF model is constructed, Loopy Belief Propagation (LBP) is then employed to perform information inference to estimate the missing data given incomplete …
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AC-RL: A Framework for Real-Time Control, Learning & Adaptation
… considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. A combination of Adaptive Control (AC) in the inner loop and a Reinforcement Learning (RL) based policy in the outer loop is proposed such that in real-time the inner-loop model …
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Dynamic Bayesian Networks for Information Fusion With Applications to Human-Computer Interfaces
… yields efficient approximate inference and parameter learning techniques applicable to a wide variety of problems. Experimental validation of the proposed approaches in the domains of gesture and speech recognition confirms the model's applicability to both unimodal and multimodal …
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A probabilistic graphical model based data compression architecture for Gaussian sources
… corruption, seamless interface with source model parameter learning, and joint homomorphic encryption-compression. This work, meant to be an exploration in a new direction in data compression, is at the intersection of Electrical Engineering and Computer Science, tying together the disciplines of …
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On the 3D point cloud for human-pose estimation
… of two steps: structure identification and parameter learning. In structure identification, we have developed a bottom-up approach to build a neural network while preserving the Bayesian-network structure. In parameter learning, we have created a part-based approach to learn synaptic weights …
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Semi-supervised and active training of conditional random fields for activity recognition
… past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We …
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Machine Learning Methods for Decision Making Inference in Healthcare
Machine learning algorithms are widely regarded as disruptive innovations. They have demonstrated superior performance in many complex domains, such as computer visions, signal processing and natural language processing. One area, in particular, in which machine learning has potential widespread …
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Sequential Modelling and Inference of High-frequency Limit Order Book with State-space Models and Monte Carlo Algorithms
… inference, regime identification and regime parameters learning requiring minimal prior assumptions. Chapter 6 focuses on the development of efficient parameter-learning algorithms for state-space models and presents three algorithms each demonstrating promising results in comparison to some …
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Generative modeling of sequential data
… regarding representation (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), and optimization. For the representation aspect, we make the following contributions: -We argue that using a multi-modal latent representation (unlike popular …
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Overcoming uncertainty for within-network relational machine learning
… videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint …
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Image Compression using Sum-Product Networks
… the source data is unknown. Existing structure learning algorithms for PGMs are inefficient for learning from large datasets and place additional constraints on the graphical model structure that diminishes a PGM’s representational power. Due to the difficulty of inference and learning in …
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New PDE models for imaging problems and applications
… is also crucial. For this sake, we consider a learning approach which estimates the optimal ratio between the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. …