In [2] Antonisse made a conjecture that unambiguous grammars are better candidates for grammar-guided genetic learning. In this paper, we empirically show that it is not always the case, especially when the structural ambiguity is boosted by semantic redundancies in the grammar. We also show that the search space (or genotype space) of grammar guided genetic programming (GGGP) is truly tree set...

8 The main challenge of facial landmark localization in real-world application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional facial shape regression, we propose a hierarchical pose regression approach, estimating the head rotation, face components, and facial landmarks hierarchically. The regression process...

The problem of data island hinders the application big in artificial intelligence model training, so researchers propose a federated learning framework. It enables training without having to centralize all central storage point. In current horizontal scheme, each participant gets final jointly trained model. No solution is proposed for scenarios where participants only provide exchange benefits...

This paper describes the SAARSHEFF systems that participated in the English Semantic Textual Similarity (STS) task in SemEval2016. We extend the work on using machine translation (MT) metrics in the STS task by automatically annotating the STS datasets with a variety of MT scores for each pair of text snippets in the STS datasets. We trained our systems using boosted tree ensembles and achieved...

background & aim: in many medical studies, one data set is used to construct the model, and to test its performance. this approach is prone to over optimization, and leads to statistics with low chance of external validity. data splitting can be used to create training and test sets but the cost is reduction in power. the aim of this study was to demonstrate the ability of bootstrap aggregating...

The covariate-balancing propensity score (CBPS) extends logistic regression to simultaneously optimize covariate balance and treatment prediction. Although the CBPS has been shown to perform well in certain settings, its performance has not been evaluated in settings specific to pharmacoepidemiology and large database research. In this study, we use both simulations and empirical data to compar...

LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here...

In this paper, we present a novel methodology to detect and recognize objects in cluttered scenes by proposing boosted contextual descriptors of landmarks in a framework of multi-class object recognition. To detect a sample of the object class, Boosted Landmarks identify landmark candidates in the image and define a constellation of contextual descriptors able to capture the spatial relationshi...