952 research outputs found
Hellinsia aldabrensis T.B. Fletcher 1910
No full textHellinsia aldabrensis (T.B. Fletcher, 1910) (Figs. 42–44) Pterophorus aldabrensis T.B. Fletcher, 1910: 403. (Type locality: Aldabra Island). Material examined: 2 ♂, 1 ♀ Uzuzu Hill, 14 – 15.12.2010, (male, slide 22687 BMNH), 2 ♂ 0 2.01. 2009, (female, slide 22688 BMNH), 2 ♂ 17–18. 0 4. 2011; 1 ♂ Mpatamanga, 16–17. 0 1.2009, Kovtunovich V. & Ustjuzhanin P. Distribution: Aldabra Island, Rep. S. Africa, Zimbabwe, Malawi. Notes. New for Malawi.Published as part of Kovtunovich, V., Ustjuzhanin, P. & Murphy, R., 2014, Plume moths of Malawi (Lepidoptera: Pterophoridae), pp. 451-494 in Zootaxa 3847 (4) on page 484, DOI: 10.11646/zootaxa.3847.4.1, http://zenodo.org/record/25005
A robust approach to model-based classification based on trimming and constraints
No full textIn a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method.Science Foundation IrelandInsight Research Centre12 month embargo - A
Anomaly and Novelty detection for robust semi-supervised learning
No full textThree important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimation using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method
Robust variable selection for model-based learning in presence of adulteration
No full textThe problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection have been proposed in model-based classification. The impact of outliers and wrongly labeled units on the determination of relevant predictors has instead received far less attention, with almost no dedicated methodologies available. Two robust variable selection approaches are introduced: one that embeds a robust classifier within a greedy-forward selection procedure and the other based on the theory of maximum likelihood estimation and irrelevance. The former recasts the feature identification as a model selection problem, while the latter regards the relevant subset as a model parameter to be estimated. The benefits of the proposed methods, in contrast with non-robust solutions, are assessed via an experiment on synthetic data. An application to a high-dimensional classification problem of contaminated spectroscopic data is presented
Fundumental One-Dimensional Analysis of Photo-Diodes
No full textTitle: Fundumental One-Dimensional Analysis of Photo-Diodes, Author: T.B. Remple, Location: Thodethe program developed by A.M. Start, in his paper, Fundamental One-Dimentional Analysis of Transistors, Philips Research Report Supplements, #4, 1976, has been modified to handle high voltage, reversed biased p-i-n photo-diodes. The physical involved in the development of stark;s model is summarized and three different p-i-n diodes are analyzed. A Schottky barrier is also analyzed by assuming the metal contact is a very highly doped semiconductor material. A listing of the program is given in the appendices, as well as a description of the program and a user's guide. Te program is written in Fortran, was run on a CDC 6400 in double precision (giving 29 digits accuracy), requiring 45 k of memory and 300 to 1000 seconds run time.ThesisMaster of Engineering (ME
Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food
No full textClassification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths
Testing for common cycles in non-stationary VARs with varied frecquency data
No full textThis paper proposes a new way for detecting the presence of common cyclical features when several time series are observed/sampled at different frequencies, hence generalizing the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The First set is implied by identities stemming from the fact that the differences of the high-frequency I(1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed VECM representations extend the results of Ghysels (2012) and are very important for determining the correct set of variables to be used in a subsequent common cycle investigation. This has some empirical implications both for the behavior of the test statistics as well as for forecasting. Empirical analyses with the quarterly real GNP and monthly industrial production indices for, respectively, the U.S. and Germany illustrate our new approach. This is also investigated in a Monte Carlo study, where we compare our proposed mixed-frequency models with models stemming from classical temporal aggregation methods
CHAINels: Journal
No full textA group of three students worked a couple of months at CHAINels for their computer science bachelor project. In these months a recommendation algorithm was designed and implemented in CHAINels. The recommendation algorithm recommends posts to a company and those posts are shown in the Journal which was also made during this project. In this report, every step of the design and implementation of the Journal and recommendation algorithm is explained.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc
#350 Home Buying Veteran: Safe or Sorry?
No full textParticipants include: Mr. T.B. King, Director, Veterans' Administration Loan Guaranty Service Mr. Edward Carr, Operative Builder and Developer; Past President, National Association of Home Builders Mr. U.K. Murphy, Past President, United States Savings and Loan League; Director, U.S. Chamber of Commerc
Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications
No full textFood authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification
performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins
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