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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
21

A Machine Learning Method Suitable for Dynamic Domains

Rowe, Michael C. (Michael Charles) 07 1900 (has links)
The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and values.
22

Efficient Machine Learning with High Order and Combinatorial Structures

Tarlow, Daniel 13 August 2013 (has links)
The overaching goal in this thesis is to develop the representational frameworks, the inference algorithms, and the learning methods necessary for the accurate modeling of domains that exhibit complex and non-local dependency structures. There are three parts to this thesis. In the first part, we develop a toolbox of high order potentials (HOPs) that are useful for defining interactions and constraints that would be inefficient or otherwise difficult to use within the standard graphical modeling framework. For each potential, we develop associated algorithms so that the type of interaction can be used efficiently in a variety of settings. We further show that this HOP toolbox is useful not only for defining models, but also for defining loss functions. In the second part, we look at the similarities and differences between special-purpose and general-purpose inference algorithms, with the aim of learning from the special-purpose algorithms so that we can build better general-purpose algorithms. Specifically, we show how to cast a popular special-purpose algorithm (graph cuts) in terms of the degrees of freedom available to a popular general-purpose algorithm (max-product belief propagation). After, we look at how to take the lessons learned and build a better general-purpose algorithm. Finally, we develop a class of model that allows for the discrete optimization algorithms studied in the previous sections (as well as other discrete optimization algorithms) to be used as the centerpoint of probabilistic models. This allows us to build probabilistic models that have fast exact inference procedures in domains where the standard probabilistic formulation would lead to intractability.
23

Efficient Machine Learning with High Order and Combinatorial Structures

Tarlow, Daniel 13 August 2013 (has links)
The overaching goal in this thesis is to develop the representational frameworks, the inference algorithms, and the learning methods necessary for the accurate modeling of domains that exhibit complex and non-local dependency structures. There are three parts to this thesis. In the first part, we develop a toolbox of high order potentials (HOPs) that are useful for defining interactions and constraints that would be inefficient or otherwise difficult to use within the standard graphical modeling framework. For each potential, we develop associated algorithms so that the type of interaction can be used efficiently in a variety of settings. We further show that this HOP toolbox is useful not only for defining models, but also for defining loss functions. In the second part, we look at the similarities and differences between special-purpose and general-purpose inference algorithms, with the aim of learning from the special-purpose algorithms so that we can build better general-purpose algorithms. Specifically, we show how to cast a popular special-purpose algorithm (graph cuts) in terms of the degrees of freedom available to a popular general-purpose algorithm (max-product belief propagation). After, we look at how to take the lessons learned and build a better general-purpose algorithm. Finally, we develop a class of model that allows for the discrete optimization algorithms studied in the previous sections (as well as other discrete optimization algorithms) to be used as the centerpoint of probabilistic models. This allows us to build probabilistic models that have fast exact inference procedures in domains where the standard probabilistic formulation would lead to intractability.
24

Spammer Detection on Online Social Networks

Amlesahwaram, Amit Anand 14 March 2013 (has links)
Twitter with its rising popularity as a micro-blogging website has inevitably attracted attention of spammers. Spammers use myriad of techniques to lure victims into clicking malicious URLs. In this thesis, we present several novel features capable of distinguishing spam accounts from legitimate accounts in real-time. The features exploit the behavioral and content entropy, bait-techniques, community-orientation, and profile characteristics of spammers. We then use supervised learning algorithms to generate models using the proposed features and show that our tool, spAmbush, can detect spammers in real-time. Our analysis reveals detection of more than 90% of spammers with less than five tweets and more than half with only a single tweet. Our feature computation has low latency and resource requirement. Our results show a 96% detection rate with only 0.01% false positive rate. We further cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.
25

Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data

Memisevic, Roland 28 July 2008 (has links)
Real world data is not random: The variability in the data-sets that arise in computer vision, signal processing and other areas is often highly constrained and governed by a number of degrees of freedom that is much smaller than the superficial dimensionality of the data. Unsupervised learning methods can be used to automatically discover the “true”, underlying structure in such data-sets and are therefore a central component in many systems that deal with high-dimensional data. In this thesis we develop several new approaches to modeling the low-dimensional structure in data. We introduce a new non-parametric framework for latent variable modelling, that in contrast to previous methods generalizes learned embeddings beyond the training data and its latent representatives. We show that the computational complexity for learning and applying the model is much smaller than that of existing methods, and we illustrate its applicability on several problems. We also show how we can introduce supervision signals into latent variable models using conditioning. Supervision signals make it possible to attach “meaning” to the axes of a latent representation and to untangle the factors that contribute to the variability in the data. We develop a model that uses conditional latent variables to extract rich distributed representations of image transformations, and we describe a new model for learning transformation features in structured supervised learning problems.
26

Feature based adaptive motion model for better localization

Bhargava, Rohan 10 April 2014 (has links)
In the 21st century, we are moving ahead in making robots a ubiquitous part of our everyday life. The need for a robot to interact with the environment has become a necessity. The interaction with the world requires a sense of it's pose. Humans clearly are very good in having a sense of their location in the world around them. The same task for robots is very difficult due to the uncertainties in the movement, limitation in sensing of the environment and complexities in the environment itself. When we close our eyes and walk we have a good estimate of our location but the same can't be said for robots. Without the help of external sensors the problem of localization becomes difficult. Humans use their vestibular system to generate cues about their movement and update their position. The same can be done for robots by using acceleration, velocity or odometry as cues to a motion model. The motion model can be represented as a distribution to account for uncertainties in the environment. The parameters to the model are typically static in the current implementation throughout the experiment. Previous work has shown that by having an online calibration method for the model has improved localization. The previous work provided a framework to build adaptive motion model and targeted land based robot and sensors. The work presented here builds on the same framework to adapt motion models for Autonomous Underwater Vehicle. We present detailed results of the framework in a simulator. The work also proposes a method for motion estimation using side sonar images. This is used as a feedback to the motion model. We validate the motion estimation approach with real world datasets.
27

Scalable kernel methods for machine learning

Kulis, Brian Joseph 09 October 2012 (has links)
Machine learning techniques are now essential for a diverse set of applications in computer vision, natural language processing, software analysis, and many other domains. As more applications emerge and the amount of data continues to grow, there is a need for increasingly powerful and scalable techniques. Kernel methods, which generalize linear learning methods to non-linear ones, have become a cornerstone for much of the recent work in machine learning and have been used successfully for many core machine learning tasks such as clustering, classification, and regression. Despite the recent popularity in kernel methods, a number of issues must be tackled in order for them to succeed on large-scale data. First, kernel methods typically require memory that grows quadratically in the number of data objects, making it difficult to scale to large data sets. Second, kernel methods depend on an appropriate kernel function--an implicit mapping to a high-dimensional space--which is not clear how to choose as it is dependent on the data. Third, in the context of data clustering, kernel methods have not been demonstrated to be practical for real-world clustering problems. This thesis explores these questions, offers some novel solutions to them, and applies the results to a number of challenging applications in computer vision and other domains. We explore two broad fundamental problems in kernel methods. First, we introduce a scalable framework for learning kernel functions based on incorporating prior knowledge from the data. This frame-work scales to very large data sets of millions of objects, can be used for a variety of complex data, and outperforms several existing techniques. In the transductive setting, the method can be used to learn low-rank kernels, whose memory requirements are linear in the number of data points. We also explore extensions of this framework and applications to image search problems, such as object recognition, human body pose estimation, and 3-d reconstructions. As a second problem, we explore the use of kernel methods for clustering. We show a mathematical equivalence between several graph cut objective functions and the weighted kernel k-means objective. This equivalence leads to the first eigenvector-free algorithm for weighted graph cuts, which is thousands of times faster than existing state-of-the-art techniques while using significantly less memory. We benchmark this algorithm against existing methods, apply it to image segmentation, and explore extensions to semi-supervised clustering. / text
28

Structured exploration for reinforcement learning

Jong, Nicholas K. 18 December 2012 (has links)
Reinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomous agents that can behave intelligently in the real world. Instead of requiring humans to determine the correct behaviors or sufficient knowledge in advance, RL algorithms allow an agent to acquire the necessary knowledge through direct experience with its environment. Early algorithms guaranteed convergence to optimal behaviors in limited domains, giving hope that simple, universal mechanisms would allow learning agents to succeed at solving a wide variety of complex problems. In practice, the field of RL has struggled to apply these techniques successfully to the full breadth and depth of real-world domains. This thesis extends the reach of RL techniques by demonstrating the synergies among certain key developments in the literature. The first of these developments is model-based exploration, which facilitates theoretical convergence guarantees in finite problems by explicitly reasoning about an agent's certainty in its understanding of its environment. A second branch of research studies function approximation, which generalizes RL to infinite problems by artificially limiting the degrees of freedom in an agent's representation of its environment. The final major advance that this thesis incorporates is hierarchical decomposition, which seeks to improve the efficiency of learning by endowing an agent's knowledge and behavior with the gross structure of its environment. Each of these ideas has intuitive appeal and sustains substantial independent research efforts, but this thesis defines the first RL agent that combines all their benefits in the general case. In showing how to combine these techniques effectively, this thesis investigates the twin issues of generalization and exploration, which lie at the heart of efficient learning. This thesis thus lays the groundwork for the next generation of RL algorithms, which will allow scientific agents to know when it suffices to estimate a plan from current data and when to accept the potential cost of running an experiment to gather new data. / text
29

Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community

Blomquist, Hanna, Möller, Johanna January 2015 (has links)
The overall purpose of this study was to find a way to identify incorrect data in Sida’s statistics about their contributions. A contribution is the financial support given by Sida to a project. The goal was to build an algorithm that determines if a contribution has a risk to be inaccurate coded, based on supervised classification methods within the area of Machine Learning. A thorough data analysis process was done in order to train a model to find hidden patterns in the data. Descriptive features containing important information about the contributions were successfully selected and used for this task. These included keywords that were retrieved from descriptions of the contributions. Two Machine learning methods, Adaboost and Support Vector Machines, were tested for ten classification models. Each model got evaluated depending on their accuracy of predicting the target variable into its correct class. A misclassified component was more likely to be incorrectly coded and was also seen as an anomaly. The Adaboost method performed better and more steadily on the majority of the models. Six classification models built with the Adaboost method were combined to one final ensemble classifier. This classifier was verified with new unseen data and an anomaly score was calculated for each component. The higher the score, the higher the risk of being anomalous. The result was a ranked list, where the most anomalous components were prioritized for further investigation of staff at Sida.
30

EBKAT : an explanation-based knowledge acquisition tool

Wusteman, Judith January 1990 (has links)
No description available.

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