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.
Results
Showing 1 to 20 of 25 for “"Unsupervised Methods"”.
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Unsupervised methods for speaker diarization
… variabilities in the data through the use of unsupervised methods. Upon initial evaluation, our system achieves state-of-the art results of 0.9% Diarization Error Rate in the diarization of two-speaker telephone conversations. The approach is then generalized to the problem of K-speaker …
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Multiview monocular depth estimation using unsupervised learning methods
Existing learned methods for monocular depth estimation use only a single view of scene for depth evaluation, so they inherently overt to their training scenes and cannot generalize well to new datasets. This thesis presents a neural network for multiview monocular depth estimation. Teaching a …
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Word sense disambiguation in clinical text
… extraction. Most approaches to the problem are unsupervised or semi-supervised because of the high cost of obtaining enough annotated data for supervised learning. In this thesis we compare the application of a semi-supervised general domain state of the art WSI method to clinical text to the …
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Time Series Anomaly Detection using Prediction-Reconstruction Mixture Errors
… between multiple variables. Most successful unsupervised methods either use single-timestamp prediction or reconstruct entire time series. However, these methods are not mutually exclusive and can each offer complementary perspectives. This work first explores the successes and limitations of …
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Context-dependent type-level models for unsupervised morpho-syntactic induction
This thesis improves unsupervised methods for part-of-speech (POS) induction and morphological word segmentation by modeling linguistic phenomena previously not used. For both tasks, we realize these linguistic intuitions with Bayesian generative models that first create a latent lexicon before …
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Development and evaluation of machine learning algorithms for biomedical applications
… network sampling; (iii) combined supervised and unsupervised methods to infer gene networks; and (iv) sampling and boosting techniques for reverse engineering gene networks. For drug sensitivity prediction problem, the dissertation presents (i) an instance selection technique and hybrid method …
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Colour analysis and the classification of fruit
… This thesis describes colour systems and methods to grade the fruit automatically via the clustering and classification methods. After investigating several approaches to automatically sort fruit based on colour, an image processing approach was taken. The colours on the fruit …
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The influence of the inclusion of biological knowledge in statistical methods to integrate multi-omics data
… omics data) together. The classical statistical methods could not address the challenges of combining multiple data types, leading to the development of ad hoc methodologies, which however depend on several factors. Among those, it is important to consider whether “prior knowledge” on the …
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Unsupervised spoken keyword spotting and learning of acoustically meaningful units
… many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this …
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Application of supervised and unsupervised learning to analysis of the arterial pressure pulse
… an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as …
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From Bones to Bytes
… osteometric measurements using machine learning methods to classify and explore sex-related structure in bone data. Supervised approaches generally performed well, with support vector machines proving the most robust under small-sample conditions, whereas unsupervised methods were primarily …
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On the Evaluation and Modelling of Context-sensitive Lexical Semantics
… data. As an outcome, I designed two novel unsupervised methods (MIRRORWIC and STATICTRANSFORM) that improve either within-word or inter-word contextualisation of the pretrained contextual models both monolingually and crosslingually. In sum, the thesis contributes to the field of …
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Exploring the application of Natural Language Processing to scientific medical cannabis publications
… cannabis research. The results indicate that the methods developed were able to effectively and accurately demonstrate conenction between cannabis plant compounds and diseases. Hence, the working code accurately reproduced the results of manual analysis. This was shown by the close similarity of …
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Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration
This research addresses the problem, can unsupervised learning generate a representation that improves on the commonly used term frequency-inverse document frequency (TF-IDF ) representation by capturing semantic relations? The analysis measures the quality of sentence classification using term …
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Towards Multi-Person 3D Pose Estimation in Natural Videos
… best hierarchy. The advantages of the proposed unsupervised methods are validated on various datasets including a lot of natural real-world scenes. For better evaluation and future research, a unique dataset called Moving camera Multi-Human interactions (MMHuman) is collected, with accurate …
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Surface Enhanced Raman Spectroscopy for Diagnostics in Ocular Health
… the diagnostic power of the assay. By combining unsupervised methods like K-means clustering with supervised techniques such as Support Vector Machines (SVM), the model effectively differentiated biofilm-present from biofilm-absent samples, achieving an overall accuracy of 92.5%, specificity of …
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Unsupervised grammar induction with Combinatory Categorial Grammars
… non-linguistic context, knowledge, or cues. Unsupervised grammar induction is the task of analyzing strings in a language to discover the latent syntactic structure of the language without access to labeled training data. Successes in unsupervised grammar induction shed light on the amount of …
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RETROSPECTIVE DESCRIPTIVE STUDY ON POST-MARKET MEDICINE QUALITY-RELATED COMPLAINTS AND THE APPLICATION OF FIELD-BASED DETECTION OF POORQUALITY MEDICINES USING NEAR-INFRARED AND RAMAN SPECTROSCOPIC SCREENING METHODS
… workflow incorporating preprocessing and unsupervised methods (Principal Component Analysis (PCA), K-means clustering, and Hierarchical Clustering Analysis (HCA)) was developed for spectral anomaly detection with findings validated using High-Performance Liquid Chromatography (HPLC). The …
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Data-Driven Language Understanding for Spoken Dialogue Systems
… induced from large textual corpora using unsupervised methods. The work presented in this thesis demonstrates how these methods can be adapted to overcome the limitations of language understanding pipelines currently used in spoken dialogue systems. The thesis starts with a discussion of …
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Using language similarities in retrieval for resource scarce languages: a study of several southern Bantu languages
… of the art preprocessing tools and retrieval methods are tailored for Web dominant languages and, accordingly, documents written in RSLs are lowly ranked and difficult to access in search results, resulting in a struggling and frustrating search experience for speakers of RSLs. In this thesis, …
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