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 23 for “"Conditional Random Fields"”.
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Gene prediction with conditional random fields
… In this thesis, I built upon the semi-Markov conditional random field framework created by DeCaprio et al. to predict protein-coding genes in DNA sequences. Several novel extensions were designed and implemented, including a 29-state model with both semi-Markov and Markov states, an N-best …
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Object recognition with latent Conditional Random Fields
… of parts to local features is modelled by a Conditional Random Field (CRF). We propose an extension of the CRF framework that incorporates hidden variables and combines class conditional CRFs into a unified framework for part-based object recognition. The random field captures spatial …
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Gene identification using phylogenetic metrics with conditional random fields
… a complete genome, discriminative models such as Conditional Random Fields (CRFs) have recently emerged, which focus specifically on the discrimination problem of gene identification, and can therefore be more powerful. One of the most attractive characteristics of these models is that their …
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Semi-supervised and active training of conditional random fields for activity recognition
… parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. …
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An Efficient Ranking and Classification Method for Linear Functions, Kernel Functions, Decision Trees, and Ensemble Methods
… optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 classification surrogate losses applied to pairs of features and outputs, resulting in improved ranking performance …
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Assessing models for de-identification of Electronic Discharge Summary Using Machine Learning tools
… 669 for training and 220 for test purpose. The Conditional Random Fields (CRF), Long Short Term Memory (LSTM) and Random Forest models were used, and the performance of each model was assessed. Findings: In order to assess each model’s performance, evaluation metrics were used to compare …
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A study of fine-grained sentence-level emotion tagging
… emotion tagging at the sentence-level and use Conditional Random Fields (CRF) to tag sentences with five emotion tags. We propose and study multiple features, including both basic features defined on a single sentence and dependency features defined on the context of a sentence. We create two …
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Combining Speech with textual methods for arabic diacritization
… on Hidden Markov Models and the textual model on Conditional Random Fields. The combination brings significant reduction in error rates across all metrics, especially in case endings, which are the most difficult to predict. It gives results superior to those of conventional methods, with …
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Multi-signal gesture recognition using body and hand poses
… we refer to Multi Information-Channel Hidden Conditional Random Fields (MIC-HCRFs). One advantage of MIC-HCRF is that it allows us to capture complex dependencies of multiple information channels more precisely than conventional approaches to the task. Our system was evaluated on the scenario …
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Bayesian Relevance for Enhanced Human-Robot Collaboration
… methods, such as Gaussian Mixture Models and Conditional Random Fields, are generally less interpretable due to their lack of causality between variables. A novel framework called Bayesian Relevance (BR) is presented for human intent prediction in HRC scenarios. The complexity of intent …
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Advanced classification methods for UAV imagery
… in urban scenarios; iii) a novel multilabel conditional random fields classification framework that exploits simultaneously spatial contextual information and cross-correlation between labels; iv) a novel spatial and structured support vector machine for multilabel image classification by …
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Toward more scalable structured models
… spaces. First, we extend Gaussian conditional random fields, traditionally unimodal and only capturing pairwise variables interactions, to model multi-modal distributions with high-order dependencies between the output space variables, while enabling exact inference and …
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Data analysis in proteomics novel computational strategies for modeling and interpreting complex mass spectrometry data
… machine learning approach is developed based on conditional random fields (CRFs). These models are capable of dealing with arbitrary sequence modeling tasks, similar to hidden Markov models (HMMs), but are far more robust to interdependent observational features, and do not require limiting …
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Efficient inference and learning for computer vision labelling problems
… the maximum a posteriori solutions of Markov and conditional random fields which can be used to model labelling problems in vision. When formulating such problems in an energy minimization framework there are three main issues that need to be addressed: (i) How to perform efficient inference to …
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Non-parametric Bayesian models for structured output prediction
… tasks, and combines attractive properties of conditional random fields (CRF), structured support vector machines, and Gaussian process (GP) classifiers. In probabilistic terms, GPstruct combines a CRF likelihood with a GP prior on factors; it can also be described as a Bayesian kernelized CRF. …
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Information extraction from text for deep domain knowledge graph population. Extracting pre-clinical outcomes in the domain of spinal cord injury
… fashion relying on statistical inference and conditional random fields at the heart of our system. The main contribution of this work is the development of a machine learning methods integrated into a holistic domain-adapted information extraction system that is capable of predicting the full …
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Spoken Language Understanding: from Spoken Utterances to Semantic Structures
… either on Stochastic Finite State Transducers or Conditional Random Fields. Joint models based on transducers are also amenable to decode word lattices generated by large vocabulary speech recognizers. We show the benefit of our approach with comparative experiments among generative, …
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Structured support vector machines learning and application in computer vision
… in computer vision. In recent years, Markov /Conditional Random Fields (MRFs/CRFs) have gained popularity for the concept of "structured" learning, by defining proper pairwise potential functions to represent the spatial correlations among neighboring pixels. In this thesis, we propose an …
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Structured support vector machines learning and application in computer vision
… in computer vision. In recent years, Markov /Conditional Random Fields (MRFs/CRFs) have gained popularity for the concept of "structured" learning, by defining proper pairwise potential functions to represent the spatial correlations among neighboring pixels. In this thesis, we propose an …
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Continuous dimensional emotion tracking in music
… work done in musicology, psychology, and other fields. However, automatic emotion prediction in music is still at its infancy and often lacks that transfer of knowledge from the other fields surrounding it. This dissertation explores automatic continuous dimensional emotion prediction in music …
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