[PDF] Top 20 Discriminative Learning Under Covariate Shift

... The discriminative model for the logistic loss is described in a prior conference publication (Bickel et al., 2007). Our original results showed that the resulting optimization problem is not con- vex. New ... See full document

... Angles ϑ corresponding to lucky β(ϑ) and to reductions in RMSE due to semi-supervised learning line-up vertically across the rows of Figure 8 (i.e., ϑ with a positive vertical co- ordinate in row 1 also have a ... See full document

... Fig. 5: Covariate shift in the EEG dataset 2A-subject A03, between training and testing input distribution for different frequency bands. (a) Mu band [8-12] Hz, and (b) Beta band [14-30] Hz. The red circles ... See full document

... or learning approaches that are able to detect the changes that may repeat overtime, to update the classifier in a timely ...as covariate shifts in the EEG signals, wherein the input data distributions ... See full document

... machine learning. However, under the covariate shift, a standard model selection technique such as cross validation (CV) (Stone, 1974; Wahba, 1990) does not work as desired; more specifically, ... See full document

... deep learning network and then enhanced with covariate shift adaptation of the principal ...deep learning network is constituted of a stack of three autoencoders and two softmax classifiers ... See full document

... the learning process infers a conditional model P(y|x): the conditional distribution of the outputs given the ...as covariate shift, where P(x) changes between training and testing, and dataset ... See full document

... Goldberg and Elhadad (2010) present a simi- lar training approach for a Dependency Parser that builds the tree-structure by recursively creating the easiest arc in a non-directional manner. This model also integrates the ... See full document

... machine learning scenarios such as covariate shift adaptation and outlier ...in covariate shift adaptation and outlier detection, and experimentally confirmed that the proposed uLSIF is ... See full document

... a learning method that gives weight to an instance of training ...data. Learning under co- variate shift is typical method for this ... See full document

... supervised learning in the original document space based on n-grams, we perform learning in a similarity ...the covariate shift problem is significantly mitigated, which enables us to build ... See full document

... reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy ...off-policy ... See full document

... supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only pos- itive and unlabeled data are accessible for learning, which is called ... See full document

... most discriminative sentences rather than similar ones that might not be critical to shape the em- ...in learning a decision hyperplane in a classification ... See full document

... model, discriminative reranking (Collins, 2000) could po- tentially improve its ...a discriminative classifier that uses global features of complete parses to identify correct interpreta- tions, a reranker ... See full document

... Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We pro- pose a novel discriminative training method that projects the raw term vectors ... See full document

... While the importance of finding a good inter- mediate representation is clear, emphasis is typi- cally placed on the later stage of extracting features over this intermediate representation, thus separat- ing ... See full document

... For the last 10 years signal model based on sparse representation has been a very active research subject. The interest in sparse representation is motivated by the great success it achieved when applied to many signal ... See full document

... A natural way of avoiding undertraining is to train sep- arate models for groups of competing features—in the driving example, one model with the ditch features, and one with the side-of-the-road features—and then ... See full document

... In this paper, we proposed a discriminative dictionary learning method for person re-identification. We conducted probability statistics of training set to divide the data into two categories, which led to ... See full document