under a specific cross-validation scheme are commonly used to evaluate their relative performances. Hadasch et al. compare the predictive abilities of contending models for MAS under several contrasting cross-validation schemes using empirical datasets on two quantitative traits with different heritabilities collected from multienvironment trials. The predictive abilities of models for MAS were...

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With the increasing size of today’s data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses nonparametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating un...

Compressive sensing (CS) is a data acquisition technique that measures sparse or compressible signals at a sampling rate lower than their Nyquist rate. Results show that sparse signals can be reconstructed using greedy algorithms, often requiring prior knowledge such as the signal sparsity or the noise level. As a substitute to prior knowledge, cross validation (CV), a statistical method that e...

In recent years, many studies have been published on data collected from social media, especially microblogs such as Twitter. However, rather few of these studies have considered evaluation methodologies that take into account the statistically dependent nature of such data, which breaks the theoretical conditions for using cross-validation. Despite concerns raised in the past about using cross...

With the increasing size of today’s data sets, finding the right parameter configuration via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming cand...

We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artiicial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art ...

RegulonDB provides curated information on the transcriptional regulatory network of Escherichia coli and contains both experimental data and computationally predicted objects. To account for the heterogeneity of these data, we introduced in version 6.0, a two-tier rating system for the strength of evidence, classifying evidence as either 'weak' or 'strong' (Gama-Castro,S., Jimenez-Jacinto,V., P...

Despite the ubiquitous nature of parentification, few instruments allow for the empirical study of the roles, responsibilities, and processes that undergird this construct. To fill this need, the researchers developed and refined a new instrument, the Parentification Inventory (PI; Hooper, 2009). A sample of 847 college students was split into two random, non-overlapping samples. For these init...