A robust approach to model-based classification based on trimming and constraints

In 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

THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB

MATLAB is a multi-paradigm proprietary programming language and numerical computing environment developed by MathWorks. Within MATLAB Integrated Development Environment (IDE) you can perform Computer-aided design (CAD), different matrix manipulations, plotting of functions and data, implementation algorithms, creation of user interfaces, and has the ability to interface with programs written in other languages1. Since, its launch in 1984 MATLAB software has not particularly been associated within the field of data science. In 2013, that changed with the launch of their new data science concentrated toolboxes that included Deep Learning, Image Processing, Computer Vision, and then a year later Statistics and Machine Learning. The main objective of my thesis was to research and explore the field of data science. More specifically pertaining to the development of an object recognition application that could be built entirely using MATLAB IDE and have a positive social impact on the deaf community. And in doing so, answering the question, could MATLAB be utilized for development of this type of application? To simultaneously answer this question while addressing my main objectives, I constructed two different object recognition protocols utilizing MATLAB_R2019 with the add-on data science tool packages. I named the protocols ASLtranslate (I) and (II). This allowed me to experiment with all of MATLAB data science toolboxes while learning the differences, benefits, and disadvantages of using multiple approaches to the same problem. The methods and approaches for the design of both versions was very similar. ASLtranslate takes in 2D image of American Sign Language (ASL) hand gestures as an input, classifies the image and then outputs its corresponding alphabet character. ASLtranslate (I) was an implementation of image category classification using machine learning methods. ASLtranslate (II) was implemented by using a deep learning method called transfer learning, done by fine-tuning a pre-trained convolutional neural network (CNN), AlexNet, to perform classification on a new collection of images

Pastoral Farmer Goals and Intensification Strategies

Focus groups were held with four pastoral sectors (sheep, dairy, deer, and beef) to investigate intensification strategies available to each sector. Focus groups first identified drivers of intensification in their sector, then identified the strategies they perceived as available, and evaluated the identified strategies in terms of favourability. For a researcher selected intensification strategy in each pastoral sector, benefits, barriers and solutions, and the relationship between farmer goals and the selected strategy was examined. The three main drivers of intensification in the sheep industry were profit, higher land values and return on capital. The researcher chosen strategy, high fecundity sheep, was viewed by the focus group as having benefits of increased financial security, increased profit, better return on capital and better land utilisation. However the strategy was seen as conflicting with other desirable goals such as lifestyle, social life, work variety, self reliance, environmental concerns and animal welfare. The three main drivers of intensification in the dairy sector were declining market prices, need for increased profit and need for increased productivity. The researcher chosen strategy, robotic milking, was viewed as having benefits of: reduced labour requirements, enhanced lifestyle, greater job satisfaction, reduce operational costs and increased profit. Implementation cost was viewed as a barrier as was the need for new specialised technical skills. The three main drivers of intensification in the deer industry were return on investment, competition from other land uses and returns per hectare compared with other pastoral sectors. The researcher chosen strategy, 100kg weaner by 1st June, had benefits of increased management options, increased profit, achievement of animals’ genetic potential, better predictability and a higher kill-out yield. The strategy presents challenges to animal welfare – an important consideration for the group. Three industry enterprises (dairy, calf rearers, and beef finishers) are involved in beef production. All three agreed that profit was the main driver for intensification. The researcher chosen strategy was dairy/beef progeny. Benefits of this strategy for the industry were: increased profit, access to prime markets, higher yielding quicker growing animals, and better behaved animals. The primary barrier to the success of this strategy was the need for co-operation across the three industry enterprises and the processors, and the need to ensure increased profits are distributed to all parts of the chain. Dairy farmers (the source of 65% of animals farmed for beef) were particular concerned about animal welfare issues and the consequent financial risks presented to their operations by this strategy.Agribusiness, Agricultural Finance, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Food Consumption/Nutrition/Food Safety, Land Economics/Use, Livestock Production/Industries, Risk and Uncertainty,

“It is through others that we become ourselves.” A study of Vygotskian play in Russian and Irish schools.

<div> <div> <div> <p>Fifty years after publishing his seminal work on play and its role in child development, Vygotskian theory is still highly influential in education, and particularly in early years. This paper presents two examples of full integration of Vygotskian principles into schools in two very different settings. Both report improvements in learning and in well-being, and exemplify the theory–practice–theory cycle, highlighting the development of new theoretical constructs arising out of putting theory firmly into practice. In both settings, the positive results have come from years of effort, in which school personnel who may have been skeptical at first, have been inspired by the impact of adopting Vygotskian play on the children they teach. The Northern Ireland study shows that at least some of the Golden Key principles (mixed-age play and enhanced home–school links) translate perfectly into very different cultural-historical contexts. </p><p>International Research in Early Childhood Education, vol. 7, no. 2, pp. 129–146</p> </div> </div> </div

Robust variable selection for model-based learning in presence of adulteration

The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection in model-based classification have been proposed. Surprisingly, the impact of outliers and wrongly labeled units on the determination of relevant predictors has received far less attention, with almost no dedicated methodologies available in the literature. In the present paper, we introduce two robust variable selection approaches: 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 concludes the paper

Self-reported measures of training exposure: Can we trust them, and how do we select them?

Editoria

Robust classification of spectroscopic data in agri-food: First analysis on the stability of results

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection