[PDF] Top 20 Learning Digital Geographies through a Graph-Based Semi-supervised Approach
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Learning Digital Geographies through a Graph-Based Semi-supervised Approach
... graphs based on spatio-temporal distance which take the temporal element of tweets into account during the construction of ...one based on a spatio-temporal Euclidean distance and one based on a ... See full document
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Graph based Semi Supervised Learning of Translation Models from Monolingual Data
... of learning translations from monolingual data is of long standing interest, and has been approached in several ways (Rapp, 1995; Callison-Burch et ...using graph- based semi-supervised ... See full document
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Matrix Completion for Graph-Based Deep Semi-Supervised Learning
... merge supervised with unsu- pervised learning methods using a deep learning ...of supervised and un- supervised cost functions by using back propagation, and at the same time prevents ... See full document
8
Efficient Graph Based Semi Supervised Learning of Structured Tagging Models
... in graph-based SSL. The standard approach for un- structured problems is to construct a graph whose vertices are labeled and unlabeled examples, and whose weighted edges encode the degree to ... See full document
10
A Graph Based Semi Supervised Approach for Analysis of Derivational Nouns in Sanskrit
... an approach for anal- ysis of derivational nouns in ...the Digital Corpus of Sanskrit, The Sanskrit Library, GRETIL, ...a semi supervised approach for identifica- tion and analysis of ... See full document
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Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data
... promising approach to solving this problem[15–17]. In a crowdsourcing-based system, each user can contribute to the construction and updating of the radio ... See full document
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Data Driven Graph Construction for Semi Supervised Graph Based Learning in NLP
... proposed approach might arise when the first-pass classifier yields confident but wrong predictions, especially for outlier samples in the original ...the graph-based learner should not simply be ... See full document
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Morpho syntactic Lexicon Generation Using Graph based Semi supervised Learning
... 2014). Graph-based learning has been used for class-instance acquisition (Talukdar and Pereira, 2010), text classification (Subramanya and Bilmes, 2008), summarization (Erkan and Radev, 2004), ... See full document
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
... of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal ...a semi-supervised framework that incorporates labeled and unlabeled data ... See full document
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Query focused Multi Document Summarization: Combining a Topic Model with Graph based Semi supervised Learning
... approaches, graph-based semi-supervised learning algorithms have been shown to be an effective way to impose a query’s influence on sentences (Zhou et al, 2003; Zhou et al, 2004; Wan et ... See full document
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Graph based Semi supervised Gene Mention Tagging
... new approach for this task, an approach that uses graph- based semi-supervised learning to train a Conditional Random Field (CRF) ...CRF-based supervised NER ... See full document
9
Graph Based Posterior Regularization for Semi Supervised Structured Prediction
... of graph-based semi-supervised learning builds on access to plentiful unsupervised data and accurate similarity measures between data examples (Zhu et ...use graph-based ... See full document
9
Graph Based Semi Supervised Learning for Natural Language Understanding
... transductive graph- based semi-supervised learning models as well as their inductive variants for ...a graph, we use a paraphrase detection ...first approach to ap- ply ... See full document
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Collective Tweet Wikification based on Semi supervised Graph Regularization
... employ graph-based semi-supervised learning algorithms (Zhu et ...defined graph, where the nodes represent a set of labeled and unlabeled instances, and the weighted edges ... See full document
11
A Graph Based Semi Supervised Learning for Question Semantic Labeling
... and learning methods to extract different salient features such as question type, event, entities, ...units based on their discourse relations via rule-based ... See full document
9
Graph Based Semi Supervised Learning Approach for Tamil POS tagging
... and graph-theoretical approaches can be em- ployed to find efficient solutions for NLP ...and graph is a natural way to capture the re- lationship between the ...entities. Graph based ... See full document
6
A Graph based Semi Supervised Learning for Question Answering
... a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate ...matching based on a question-classifier, etc. We implement a ... See full document
9
Graph based Learning for Statistical Machine Translation
... the graph. This choice is theoretically motivated—a similarity graph for regression should have not only “sources” (good nodes with high value of r) but also “sinks” (counterparts for the ...rich ... See full document
9
A Review on health care examination records using data mining
... of learning the design for risk of unhealthy life in future lies in the unlabeled data which is a very integral part of the dataset which consist of the person’s data who is perfectly healthy and whose condition ... See full document
5
Semi supervised Graph based Genre Classification for Web Pages
... sifier for the cosine similarity equal or greater than 0.8 which was chosen on the validation data. It must be noted that the result of the multi-class min-cut algorithm when we used all the neigh- bouring pages was much ... See full document
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