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 20 of 1060 for “"learning algorithms"”.
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High-throughput machine learning algorithms
The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of …
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Stability of machine learning algorithms
… is often the primary criterion for evaluating a learning algorithm. In this thesis, I will introduce novel concepts of stability into the machine learning community. A learning algorithm is said to be stable if it produces consistent predictions with respect to small perturbation of training …
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Information-theoretic bounds in learning algorithms
… focus of this thesis is on understanding machine learning algorithms from an information-theoretic point of view. More specifically, we apply information-theoretic tools to construct performance bounds for the learning algorithms, with the goal of deepening the understanding of current algorithms …
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Transfer learning algorithms for image classification
… To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) Exploit labeled data from related categories. In the first part of this thesis we show how to use the structure learning framework (Ando and Zhang, 2005) to learn …
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Coactive Learning Algorithms for Constructive Preference Elicitation
… making it necessary to employ specialized algorithms for preference elicitation. Preference elicitation algorithms interactively build a utility model of the user preferences and then recommend the instances with the highest utility. Preference elicitation is especially effective when …
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On efficient learning algorithms for neural networks.
Inductive Inference Learning can be described in terms of finding a good approximation to some unknown classification rule f, based on a pre-classified set of training examples $\langle$x,f(x)$\rangle.$ One particular class of learning systems that has attracted much attention recently is the class …
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Meta-RaPS Hybridization with Machine Learning Algorithms
… as Meta-RaPS, by integrating it with machine learning algorithms. Introducing a new metaheuristic algorithm starts with demonstrating its performance. This is accomplished by using the new algorithm to solve various combinatorial optimization problems in their basic form. The next stage …
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Machine Learning Algorithms for Improved Glaucoma Diagnosis
… advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic performance of the applied machine learning classification algorithms was shown to depend primarily on …
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Fairness and Privacy in Machine Learning Algorithms
… but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine …
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Probabilistic machine learning algorithms for molecule discovery
… will be gained from each test. In machine learning, this approach is typically called Bayesian optimisation and has been studied for many other problems, such as tuning hyperparameters of machine learning models. Although in principle Bayesian optimisation can be straightforwardly applied …
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ModelDiff: A Framework for Comparing Learning Algorithms
We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the …
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Inference-Time Learning Algorithms of Language Models
… can perform complex tasks through in-context learning (ICL)—they can adapt to a task via examples provided in their input without any parameter updates. However, fundamental questions remain about when this adaptation works, what algorithms underlie it, and how to improve it. This thesis …
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Enhancing Learning Algorithms via Sublinear-Time Methods
Our society increasingly relies on algorithms and data analysis to make critical decisions. Yet, almost all work in the theory of supervised learning has long relied on the following two assumptions: 1. Distributional assumptions: data satisfies conditions such as Gaussianity or uniformity. 2. No …
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Machine Learning Algorithms for Geometry Processing by Example
This thesis proposes machine learning algorithms for processing geometry by example. Each algorithm takes as input a collection of shapes along with exemplar values of target properties related to shape processing tasks. The goal of the algorithms is to output a function that maps from the shape …
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SCALABLE GRAPH REPRESENTATIONAL LEARNING ALGORITHMS FOR NETWORK MEDICINE
… bio-technologies represents a central Machine Learning and Computational Biology challenge. Indeed several compelling problems in Network Medicine, ranging from gene-disease prioritization to drug-target prediction or drug repurposing can be modelled as node label or edge prediction problems in …
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