Improved Second-Order Bounds for Prediction with Expert Advice

This work studies external regret in sequential prediction games with both positive and negative payoffs. External regret measures the difference between the payoff obtained by the forecasting strategy and the payoff of the best action. In this setting, we derive new and sharper regret bounds for the well-known exponentially weighted average forecaster and for a new forecaster with a different multiplicative update rule. Our analysis has two main advantages: first, no preliminary knowledge about the payoff sequence is needed, not even its range; second, our bounds are expressed in terms of sums of squared payoffs, replacing larger first-order quantities appearing in previous bounds. In addition, our most refined bounds have the natural and desirable property of being stable under rescalings and general translations of the payoff sequence

Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Hierarchical cost-sensitive algorithms for genome-wide gene function prediction

In this work we propose new ensemble methods for the hierarchical classification of gene functions. Our methods exploit the hierarchical relationships between the classes in different ways: each ensemble node is trained \u201clocally\u201d, according to its position in the hierarchy; moreover, in the evaluation phase the set of predicted annotations is built so to minimize a global loss function defined over the hierarchy. We also address the problem of sparsity of annotations by introducing a cost- sensitive parameter that allows to control the precision-recall trade-off. Experiments with the model organism S. cerevisiae, using the FunCat taxonomy and 7 biomolecular data sets, reveal a significant advantage of our techniques over \u201cflat\u201d and cost-insensitive hierarchical ensembles

Leading strategies in competitive on-line prediction

We start from a simple asymptotic result for the problem of on-line regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a "leading prediction strategy", which not only asymptotically performs at least as well as any continuous limited-memory strategy but also satisfies the property that the excess loss of any continuous limited-memory strategy is determined by how closely it imitates the leading strategy. More specifically, for any class of prediction strategies constituting a reproducing kernel Hilbert space we construct a leading strategy, in the sense that the loss of any prediction strategy whose norm is not too large is determined by how closely it imitates the leading strategy. This result is extended to the loss functions given by Bregman divergences and by strictly proper scoring rules.Comment: 20 pages; a conference version is to appear in the ALT'2006 proceeding

Effects of processing on polyphenolic and volatile composition and fruit quality of clery strawberries

Strawberries belonging to cultivar Clery (Fragaria x ananassa (Duchesne ex Weston)), cultivated in central Italy were subjected to a multi‐methodological experimental study. Fresh and defrosted strawberries were exposed to different processing methods, such as homogenization, thermal and microwave treatments. The homogenate samples were submitted to CIEL*a*b* color analysis and Head‐Space GC/MS analysis to determine the impact of these procedures on phytochemical composition. Furthermore, the corresponding strawberry hydroalcoholic extracts were further analyzed by HPLC‐DAD for secondary metabolites quantification and by means of spectrophotometric in vitro assays to evaluate their total phenolic and total flavonoid contents and antioxidant activity. These chemical investigations confirmed the richness in bioactive metabolites supporting the extraordinary healthy potential of this fruit as a food ingredient, as well as functional food, highlighting the strong influence of the processing steps which could negatively impact on the polyphenol composition. Despite a more brilliant red color and aroma preservation, nonpasteurized samples were characterized by a lower content of polyphenols and antioxidant activity with respect to pasteurized samples, as also suggested by the PCA analysis of the collected data