[PDF] Top 20 Semi-described and semi-supervised learning with Gaussian processes

... a semi- supervised GP framework where features are extracted from all available information and, subsequently, are given as inputs to a discriminative ...a Gaussian distribution, ... See full document

... Most studies found in the literature try to classify plankton, which, although not exactly the focus of this work, shares some similarities with our goal. Blaschko et al. [1] presented a comparison of supervised ... See full document

... as described in a state-of-the-art feature-based system (Zhou et al 2005): word, entity type, mention level, overlap, base phrase chunking, dependency tree, parse tree and semantic ... See full document

... multivariate gaussian distributions of unknown class prior and position ...examines learning the parameters of both a single gaussian and a GMM when labels are ...on semi-supervised ... See full document

... The automatic summarization procedure is shown in Figure 1. First, each input sentence is examined by going through the pre-specified fea- ture functions. The classification model will then predict the importance of each ... See full document

... In conclusion, our proposed methodology achieves the desired objective of delivering high per- formance and is highly competitive against the top performers in the literature, where the loss U ( · ) plays a critical role ... See full document

... A major departure from much previous work is that we do not attempt to encode variation as string-changing operations, say by string edits (Dreyer and Eisner, 2011) or transformation rules (Lind´en, 2008; Durrett and ... See full document

... For training data of the paraphrase generation sys- tem, we use both general and in-domain para- phrase corpora. The in-domain paraphrase corpus, as described in Section 4, contains 1M paraphrase pairs that fit ... See full document

... (16) amounts to taking the uniform distribution as ‘base measure’ according to standard terminology of Dirichlet processes. We will later see that changing the base measure pro- vides a simple way to define an SSL ... See full document

... means supervised MMI- training with labeled data only, and the purely gener- ative method with α = 0 means extra several rounds of supervised MLE-training if the convergence cri- terion is not ... See full document

... Earlier work in semi-supervised learning assumes that there are two classes, and in each class there is a Gaussian distribution. Hence we assume that the complete data comes from a mixture ... See full document

... Our text corpus was from a 200-million page web crawl. We parsed the HTML, filtered out non- English pages using a stop word ratio threshold, then filtered out web spam and adult content using a ‘bad word’ list. The ... See full document

... Active Learning (AL) has already been shown to markedly reduce the anno- tation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the ... See full document

... 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

... We propose a sequence labeling frame- work with a secondary training objec- tive, learning to predict surrounding words for every word in the dataset. This lan- guage modeling objective incentivises the system to ... See full document

... the semi- supervised learning approach which aims to exploit unlabeled instances in the learning process to enhance the learnt classifier when there are a few labeled ...the learning ... See full document

... Semi-supervised learning algorithms: (1) The first member algorithm is called self-trainingFS, proposed by Gao et ...graph-based semi-supervised learning approach, proposed by ... See full document

... 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

... Experiments on chunking and NER showed that based on the same embeddings, the compound fea- tures managed to achieve better performances. Moreover, we proposed analyses to reveal the rea- sons for the improvements of ... See full document

... 2) semi-supervised learning by leveraging unlabeled data improves the generation performances more, indicating that our proposed method is reasonably effective when being tested on cross-domain ... See full document