We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the a...

In this paper, we address the problem of predicting the usage of home appliances where a key challenge is to model the everyday routine of homeowners and the inter–dependency between the use of different appliances. In particular, given an efficient day–ahead prediction of electrical usage, home energy management systems can suggest homeowners when is the best time to run appliances in order to...

We consider two reverse logistics systems where returned products are as good as new. For the first system, the product return flow is independent of the demand flow.We prove that the optimal policy is of base-stock type and we establish monotonicity results for the optimal base-stock levels, with respect to the system parameters (arrival rate, production rate, return rate, production cost, los...

A longitudinal survey of the social support of patients attending one geriatric day hospital confirmed an impression that support was not being adjusted to meet the changing degrees of dependency among the patients. An experimental study, in which the social support available to a small group of consecutive new admissions was reduced, showed that their dependency scores were not significantly d...

In this paper, we present a straightforward strategy for transferring dependency parsers across languages. The proposed method learns a parser from partially annotated data obtained through the projection of annotations across unambiguous word alignments. It does not rely on any modeling of the reliability of dependency and/or alignment links and is therefore easy to implement and parameter fre...

A POS-tagger can be used in front of a parser to reduce the number of combinations of possible dependency trees which, in the majority, give spurious analyses. In the paper we compare the results of the addition of three morphological taggers to the parser of the CDG Lab. The experimental results show that these models perform better than the model which do not use a morphological tagger at the...

Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a ...

We propose using large-scale clustering of dependency relations between verbs and multiword nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since dependency relations capture the semantics of MNs well, the MN clusters constructed by using dependency relations should serve as a good gazetteer. However, the high level of computational cost has prevented the use of cluster...

Product development process can be viewed as a set of sub-processes with stronger interrelated dependency relationships. In this article, the quantitative and qualitative dependency measures of serial and parallel product development processes are analyzed. The neural-network-driven fuzzy reasoning mechanism of dependency relationships is developed in the case that there is no sufficient quanti...

Cloud computing has made the dream of scalability of resources on demand come true. As the usage of the resources on the cloud involves cost, their optimal utilization is vital. Various scheduling algorithms are being designed and implemented seamlessly to achieve this goal. One of the factors that have a high impact on the scheduling algorithm design is the dependency of the tasks. Dependency ...