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 69 for “"Distance Metric"”.
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Bayesian distance metric learning on i-vector for speaker verification
This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an …
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Lower bounds for embedding the Earth Mover Distance metric into normed spaces
… a lower bounds for embedding the Earth Mover Distance (EMID) metric into normed spaces. The EMID is a metric over two distributions where one is a mass of earth spread out in space and the other is a collection of holes in that same space. The EMD between these two distributions is defined as …
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A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric
… an ensemble of models with locally modified distance weighting. An empirical study conducted on 10 standard databases from the UCI repository shows that this new Boosted k-NN algorithm has increased generalization accuracy in the majority of the datasets and never performs worse than standard …
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Detection of side-channel communication in a mobile ad-hoc network environment using the Hamming distance metric
… who are not the intended recipient. A Hamming Distance (HD) difference between the FCS values of purposely corrupted and naturally corrupted frames is proposed as a metric for the detection of side channel communication. In theory, it should be possible to recognize purposely corrupted frames …
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ULIME: Uniformly weighted Local Interpretable Model-agnostic Explanations for Image Classifiers
… evaluating models solely on standard numerical metrics, which may result in a model performing seemingly well on a dataset but for the wrong reasons. It is particularly critical to have transparency in medical image analysis due to the importance those decisions have. This is because health care …
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Graph factorization and pseudofactorization with applications to hypercube embeddings
… it is useful to determine if a particular distance metric can be broken down into other metrics about which more is known. In particular, if a metric can be embedded into a hypercube, the plethora of preexisting knowledge about the structure of a hypercube can provide knowledge about the …
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Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications
… However, little research has been done on the distance metric used by shapelets. This distance metric is critical, as it is the sole feature a shapelet uses to discriminate between samples from different classes. This thesis details two contributions to time series shapelets. The first, …
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Statistical Learning Methods for Challenges arised from Self-Reported Data
… overcome these limitations, we propose a novel distance metric tailored specifically for SR questionnaire data. This distance incorporates the correlation distance with other elementary distances for clustering data of mixed type, which outperforms existing metrics in handling mixed data when SR …
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Randomness In Tree Ensemble Methods
… tree ensemble acts by forming a locally adaptive distance metric [Lin and Jeon, 2006]. We generalize this claim to include all tree ensembles methods and argue that this insight can help to explain the exceptional performance of tree ensemble methods. Finally, we illustrate the use of tree …
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Randomness In Tree Ensemble Methods
… tree ensemble acts by forming a locally adaptive distance metric [Lin and Jeon, 2006]. We generalize this claim to include all tree ensembles methods and argue that this insight can help to explain the exceptional performance of tree ensemble methods. Finally, we illustrate the use of tree …
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Investigation of new learning methods for visual recognition
… Finally, a new Score space based multiple Metric Learning (SML) method is presented for a challenging visual recognition application, namely, recognizing kinship relations or kinship verification. The proposed SML method, which goes beyond the conventional Mahalanobis distance metric …
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Regularized algorithms for ranking, and manifold learning for related tasks
… manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking …
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Connecting Deep Learning Models to the Human Brain
… about how closely the computations and geometric structure of these models mirror that of the human brain. Our method starts with measuring brain activity in response to vision and language stimuli and then exposes these stimuli to deep learning models to collect their internal activations. …
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Machine Learning for High-Energy Collider Physics
… In the second part of this thesis, I define a distance metric between collider events based on optimal transport that allows for a rigorous construction of "event space" and its corresponding geometry. Using public datasets provided by the CMS collaboration, I explore this metric on a dataset …
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Understanding the role of referential processing in sentence complexity
… and further develop the DLT's discourse-based distance metric for computing locality. The experiments presented here were designed to investigate the referential processing load imposed by relating noun phrase (NP) anaphors to their antecedents and to discover the ramifications of increased …
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Feature-level fusion in multimodal biometrics
Multimodal biometric systems utilize the evidence presented by multiple biometric modalities (e.g., face and fingerprint, multiple fingers of a user, multiple impressions of a single finger, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can …
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Robust motion estimation techniques
… Based on this intensity profile, we develop a distance metric for local motion estimation. This scheme can be further extended for global camera estimation. Our extensive experimental results demonstrate that the proposed motion estimation scheme based on intensity profile outperforms …
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Matching and compressing sequences of visual hulls
… to define a visual hull graph, we use a simple distance metric for pose by calculating Chamfer distances between 2D shape contours. At each frame of our graph, we store a view independent 3D pose and calculate the transition probability to any other frame based on similarity of pose. To test our …
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Model reduction for Hidden Markov models
… at hand. In the Jump Linear Systems case, a distance metric based on randomization of the parametric input is introduced. The main point of the reduction algorithm lies in the formulation of two dissipation inequalities, which in conjunction with a suitably defined storage function enable the …
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A tale of two time series methods : representation learning for improved distance and risk metrics
… learning for time series in two areas: metric learning and risk stratification. We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy, a simple and scalable distance metric learning method for multivariate time …
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