[PDF] Top 20 Distribution-Based Semi-Supervised Learning for Activity Recognition
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Distribution-Based Semi-Supervised Learning for Activity Recognition
... of activity recognition, Stikic, Larlus, and Schiele (2009) proposed a multi-graph based semi- supervised approach named GLSVM, where each graph propagates different information of ...a ... See full document
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A Graph Based Semi Supervised Learning for Question Semantic Labeling
... Thus, instead of fixing the number of labeled records and varying the number of unlabeled points, we pro- pose to fix the percentage of unlabeled points in training dataset. We hypothetically use unselected part of the ... See full document
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A Review on health care examination records using data mining
... by Semi Supervised Learning. Semi-Supervised Learning is a situation in which in your training data some of the samples are not ...The semi-supervised estimators ... See full document
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An Efficient Face Detection and Recognition Method Based on Semi Supervised Learning with Improved LPP Projection Method
... Machine learning and pattern recognition methods play a vital role in face detection and recognition from an ...and recognition method based on improved locality preserving projection ... See full document
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Matrix Completion for Graph-Based Deep Semi-Supervised Learning
... Transfer Learning (TL) and 2) Semi- Supervised Learning ...task learning via transfer of knowledge from a related task which has already been ...discriminative learning methods ... See full document
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Chinese Short Text Categorization Based on Semi Supervised Learning
... image recognition and other fields ...is based on boosting was proposed by RE Schapire and Y Singer [12], which laid a foundation for later researchers to study text classification using boosting algorithm, ... See full document
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A Survey on the Enhancement of Video Action Recognition using Semi Supervised Learning
... sparse based model has been developed for the human action ...action recognition. Thirdly, we defines a sparse model based on a joint L2,1-norm form which has been proposed to perform the feature ... See full document
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Dual Semi-Supervised Learning for Facial Action Unit Recognition
... For semi-supervised AU recognition scenarios without expressions, label smoothness or AU dependencies are ex- ploited to handle missing ...AU recognition. This method can be naturally extended ... See full document
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A Graph based Semi Supervised Learning for Question Answering
... inference recognition as classification problem to determine if a ques- tion text follows candidate ...in semi-supervised learning (SSL) environment, with an emphasis on graph-based ... See full document
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Supervised and Semi Supervised Sequence Learning for Recognition of Requisite Part and Effectuation Part in Law Sentences
... There are two reasons that may explain why the Bunsetsu-based model is better than the word-based model. The first reason is that Bunsetsus are ba- sic units in analyzing Japanese (in fact, dependency ... See full document
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Chinese Named Entity Recognition with Graph based Semi supervised Learning Model
... Named entity recognition (NER) can be regarded as a sub-task of the information extraction, and plays an important role in the natural language processing literature. The NER challenge has attracted a lot of ... See full document
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LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition
... The intuition behind the 4th and 5th criteria is to take into account both the pairwise similarity between an unassigned vertex and its closest assigned vertex, and the accumulative dissimilarity between that unassigned ... See full document
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Domain Adaptation with Adversarial Training and Graph Embeddings
... sarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to lever- age unlabeled data within a single uni- fied ... See full document
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Semi supervised Speech Act Recognition in Emails and Forums
... investigated semi-supervised speech act recognition in email and forum ...of learning to recognize speech acts us- ing labeled and unlabeled ...that semi-supervised ... See full document
10
Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking
... This processing is performed prior to using an edge-sensitive Hough circle detector. The approach can easily be adapted for other domains where image segmentation is desirable, or inverted for use when sub-pixel ... See full document
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A Semi-supervised Learning Approach to Arabic Named Entity Recognition
... a semi- supervised algorithm for identifying Named Entities (NEs) in Arabic ...the semi-supervised pattern-based learning approach to Arabic Named Entity Recog- nition ...simple ... See full document
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Semi supervised Learning for Vietnamese Named Entity Recognition using Online Conditional Random Fields
... entity recognition problem in the Vietnamese ...system based on conditional ran- dom fields and address one of its chal- lenges: how to combine labeled and un- labeled data to create a stronger ...online ... See full document
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A Prototype Multiview Approach for Reduction of False alarm rate in Network Intrusion Detection System
... of semi- supervised learning as it can utilize both labeled data and unlabeled data without human ...Thus, semi-supervised learning can greatly reduce the workload of analysts ... See full document
11
Extractive Summarization Using Supervised and Semi Supervised Learning
... Supervised learning approaches normally achieve good performance but require manually labeled ...sifiers based on different feature ...classifiers based on same feature ... See full document
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Active Learning Based Elicitation for Semi Supervised Word Alignment
... Re-training the word alignment models after elic- iting every individual alignment link is infeasible. In our data set of 21,863 sentences with 588,075 links, it would be computationally intensive to re- train after ... See full document
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