Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Cereal-based wholecrop silages: a potential conservation measure for farmland birds in pastoral landscapes

Declines of farmland birds have been pronounced in landscapes dominated by lowland livestock production and densities of seed-eating birds are particularly low in such areas. Modern livestock production often entails a simple cropping system dominated by ley grassland and maize grown for animal feed. These crops often lack invertebrate and seed resources for foraging birds and can be hostile nesting environments. Cereal-based wholecrop silages (CBWCS) offer potential benefits for farmland birds because they can be grown with minimal herbicide applications and can be spring-sown with following winter stubbles. We compared the biodiversity benefits and agronomic yields of winter-sown wheat and spring-sown barley as alternatives to grass and maize silage in intensive dairy livestock systems. Seed-eating birds foraged mainly in CBWCS fields during summer, and mainly on barley stubbles during winter and this reflected the higher densities of seed-bearing plants therein. Maize and grass fields lacked seed-bearing vegetation and were strongly avoided by most seed-eating birds. Production costs of CBWCS are similar to those of maize and lower than those of grass silage. Selective (rather than broad-spectrum) herbicide application on spring barley crops increased forb cover, reduced yields (by 11%) but caused only a small (<4%) increase in production costs. CBWCS grown with selective herbicide and with following winter stubbles offer a practical conservation measure for seed-eating farmland birds in landscapes dominated by intensively-managed grassland and maize. However, the relatively early harvesting of CBWCS could destroy a significant proportion of breeding attempts of late-nesting species like corn bunting (Emberiza calandra) or yellow wagtail (Motocilla flava). Where late-breeding species are likely to nest in CBWCS fields, harvesting should be delayed until most nesting attempts have been completed (e.g. until after 1st August in southern Britain). (C) 2010 Elsevier Ltd. All rights reserved.

Developing a brand performance measure for financial services brands

With no universal approach for measuring brand performance, we show how a consumer-based brand measure was developed for corporate financial services brands. Churchill's paradigm was adopted. A literature review and 20 depth interviews with experts suggested that brand loyalty, consumer satisfaction and reputation constitute the brand performance measure. Ten financial services organisations provided access to their consumers. Following a postal survey, 600 questionnaires were analysed through principal components analysis to identify the consumer-based measure. Further testing revealed this to be a valid and reliable brand performance measure.

The Huntington's Disease health-related Quality of Life questionnaire (HDQoL): a disease-specific measure of health-related quality of life

Hocaoglu MB, Gaffan EA, Ho AK. The Huntington's disease health-related quality of life questionnaire: a disease-specific measure of health-related quality of life. Huntington's disease (HD) is a genetic neurodegenerative disorder characterized by motor, cognitive and psychiatric disturbances, and yet there is no disease-specific patient-reported health-related quality of life outcome measure for patients. Our aim was to develop and validate such an instrument, i.e. the Huntington's Disease health-related Quality of Life questionnaire (HDQoL), to capture the true impact of living with this disease. Semi-structured interviews were conducted with the full spectrum of people living with HD, to form a pool of items, which were then examined in a larger sample prior to data-driven item reduction. We provide the statistical basis for the extraction of three different sets of scales from the HDQoL, and present validation and psychometric data on these scales using a sample of 152 participants living with HD. These new patient-derived scales provide promising patient-reported outcome measures for HD.

Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.

Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

A new error measure for forecasts of household-level, high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.

J-measure based hybrid pruning for complexity reduction in classification rules

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms.

The STRATADAPT scale: a measure of marketing strategy adaptation to international business markets

Purpose – The development of marketing strategies optimally adjusted to export markets has been a vitally important topic for both managers and academics for about five decades. However, there is no agreement in the literature about which elements integrate marketing strategy and which components of domestic strategies should be adapted to export markets. The purpose of this paper is to develop a new scale – STRATADAPT. Design/methodology/approach – Results from a sample of small and medium-sized industrial exporting firms support a four-dimensional scale – product, promotion, price, and distribution strategies – of 30 items. The scale presents evidence of composite reliability as well as discriminant and nomological validity. Findings – Findings reveal that all four dimensions of marketing strategy adaptation are positively associated with the amount of the firm's financial resources allocated to export activity. Practical implications – The STRATADAPT scale may assist managers in developing better international marketing strategies as well as in planning more accurate and efficient marketing programs across markets. Originality/value – This study develops a new scale, the STRATADAPT scale, which is a broad measure of export marketing strategy adaptation.