[PDF] Top 20 Semi-Supervised Learning with Measure Propagation
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Semi-Supervised Learning with Measure Propagation
... Label Priors: This is more akin to the classical integration of priors within a Bayesian learn- ing setting. There has been some work in the past directed towards integrating priors for para- metric (non-graph-based) SSL ... See full document
60
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... to supervised word sense dis- ambiguation ...label propagation based semi- supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to ... See full document
8
Extractive Summarization Using Supervised and Semi Supervised Learning
... a learning-based ap- proach to combine various sentence fea- ...features measure a sentence based on content- conveying ...encouraging, supervised learning approach requires much labeled ... See full document
8
Semi-described and semi-supervised learning with Gaussian processes
... as learning pipelines that rely on correct propagation of uncertainty between each ...observations: semi-supervised learning, auto-regressive iterative fore- casting and, finally, a ... See full document
11
Co training for Semi supervised Sentiment Classification Based on Dual view Bags of words Representation
... In supervised sentiment classification, many ap- proaches have been proposed in addressing the negation problem (Pang et ...in semi-supervised sentiment classifica- tion, most of the current ... See full document
10
Coupling Label Propagation and Constraints for Temporal Fact Extraction
... coupled semi-supervised learning for information extraction (IE) is NELL (Carlson et ...label propagation as a semi-supervised learning strat- egy, but does not ... See full document
5
Semi Supervised Learning of Concatenative Morphology
... We used the English and Finnish data sets from Competition 1 of Morpho Challenge 2009 (Ku- rimo et al., 2009). Both are extracted from a three million sentence corpora. For English, there were 62 185 728 word tokens and ... See full document
9
Semi Supervised Learning for Relation Extraction
... The idea behind our LP algorithm via bootstrapped support vectors is that, instead of propagating la- bels through all the available labeled data, our method propagates labels through critical instances in both the ... See full document
8
Relation Extraction Using Label Propagation Based Semi Supervised Learning
... based semi-supervised learning algorithms for relation ...label propagation algorithm (LP) (Zhu and Ghahramani, 2002) for relation extraction ...the propagation process ... See full document
8
Towards Automated Semi-Supervised Learning
... and measure flex- ibility (Li, Kwok, and Zhou ...the learning process, which do not finalize a systematical solution and are not automated ...automated learning system ( AUTO - SSL ) for ... See full document
8
A New Homogeneity Inter Clusters Measure in Semi Supervised Clustering
... a semi-supervised clustering algorithm essentially based on the modification of the distance between clusters according to the must-link andcannot link ... See full document
9
Unsupervised Cross Lingual Scaling of Political Texts
... the most dissimilar political positions because: (1) our metrics of semantic similarity are imperfect, i.e., the scores they produce are not the gold stan- dard semantic similarities, but even if they were (2) we do not ... See full document
6
A Review on health care examination records using data mining
... supervised learning algorithmic rule mentioned to as SHG- Health for risk predictions to categorize increasingly developing scenario with the bulk of the information unlabeled Wide-ranging experiments ... See full document
5
Unbiased Generative Semi-Supervised Learning
... We begin by considering the highly influential work by Castelli and Cover (1995, 1996). This looks not at a particular semi-supervised algorithm, but rather at a slightly more general question of when ... See full document
77
Large Margin Semi-supervised Learning
... novel learning theory is developed to quantify SPSI’s generalization error as a function of complexity of the class of candidate decision functions, the sample sizes (n l ,n u ), and the ... See full document
25
Three phase training to address data sparsity in Neural Machine Translation
... Data sparsity is a challenging problem in NMT, especially for resource-scarce language pairs. In this paper, we proposed an inte- grated approach to reduce the impact of data sparsity in NMT, using only little amount of ... See full document
10
Distributed Semi-supervised Learning with Kernel Ridge Regression
... distributed learning algorithms by allowing more local processors while achieving optimal learning ...optimal learning rates for distributed learning algorithms are achievable only when f ρ ∈ ... See full document
22
Semi Supervised Learning for Neural Machine Translation
... Autoencoders and their variants have been widely used in unsupervised deep learning ((Vincent et al., 2010; Socher et al., 2011; Ammar et al., 2014), just to name a few). Among them, Socher et al. (2011)’s ... See full document
10
Semi Supervised Learning for Neural Keyphrase Generation
... Keyphrase Extraction and Generation. Early work mostly focuses on the keyphrase extrac- tion task, and a two-step strategy is typically designed. Specifically, a large pool of candi- date phrases are first extracted ... See full document
12
Semi supervised learning of morphological paradigms and lexicons
... In contrast to many machine learning ap- proaches that address the problem of paradigm ex- traction, the current method is intended to produce human-readable output of its generalizations. That is, the paradigms ... See full document
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