Agricultural education consists of three components: classroom instruction, FFA, and supervised agricultural experience (SAE). SAE is the experiential learning component in which students apply agricultural principles and concepts. The purpose of this study was to identify the perceptions of Kentucky high school agriculture teachers toward the value of SAE, the quality components of SAE, and teacher satisfaction with SAE. A secondary purpose was to determine the status of SAE participation in Kentucky. This study concluded that Kentucky agriculture teachers perceive SAE as a valuable component of agricultural education. Moreover, teachers were in agreement with accepted quality standards for SAE programs, but the findings implied that other quality indicators may be valid. Furthermore, teachers were not satisfied with their SAE programs. A slight majority of students in Kentucky agricultural education programs have a SAE with the bulk of those SAEs categorized as either placement or entrepreneurship SAEs. Based on the conclusions, the author recommended that a SAE task force be created to address concerns related to SAE participation, student and teacher motivation to conduct SAE, state rewards for SAEs, and assessing the quality of SAE programs in Kentucky. KEYWORDS: Agricultural

This thesis investigates the boundary of human rationality and how psychological processes interact with underlying regularities in the environment and affect beliefs and achievement. Two common modes in everyday experiential learning, supervised and unsupervised learning were hypothesized to tap different ecological and epistemological approaches to human adaptation; the Brunswikian and the Gibsonian approach. In addition, they were expected to be differentially effective for achievement depending on underlying regularities in the task environment. The first approach assumes that people use top-down processes and learn from hypothesis testing and external feedback, while the latter assumes that people are receptive to environmental stimuli and learn from bottom-up processes, without mediating inferences and support from external feedback, only exploratory observations and actions. Study I investigates selective supervised learning and showed that biased beliefs arise when people store inferences about category members when information is partially absent. This constructivist coding of pseudo-exemplars in memory yields a conservative bias in the relative frequency of targeted category members when the information is constrained by the decision maker’s own selective sampling behavior, suggesting that niche picking and risk aversion contribute to conservatism or inertia in human belief systems. However, a liberal bias in the relative frequency of targeted category members is more likely when information is constrained by the external environment. This result suggests that highly exaggerated beliefs and risky behaviors may be more likely in environments where information is systematically manipulated, for example when positive examples are highlighted to convey a favorable image while negative examples are systematically withheld from the public eye. Study II provides support that the learning modes engage different processes. Supervised learning is more accurate in less complex linear task environments, while unsupervised learning is more accurate in complex nonlinear task environments. Study III provides further support for abstraction based on hypothesis testing in supervised learning, and abstraction based on receptive bottom-up processes in unsupervised learning that aimed to form ideal prototypes as highly valid reference points stored in memory. The studies support previous proposals that integrating the Brunswikian and the Gibsonian approach can broaden the scope of psychological research and scientific inquiry.

Pattern recognition in urban areas is one of the most challenging issues in classifying satellite remote sensing data. Parametric pixel-by-pixel classification algorithms tend to perform poorly in this context. This is because urban areas comprise a complex spatial assemblage of disparate land cover types - including built structures, numerous vegetation types, bare soil and water bodies. Thus, there is a need for more powerful spectral pattern recognition techniques, utilizing pixel-by-pixel spectral information as the basis for automated urban land cover detection. This paper adopts the multi-layer perceptron classifier suggested and implemented in [5]. The objective of this study is to analyse the performance and stability of this classifier - trained and tested for supervised classification (8 a priori given land use classes) of a Landsat-5 TM image (270 x 360 pixels) from the city of Vienna and its northern surroundings - along with varying the training data set in the single-training-site case. The performance is measured in terms of total classification, map user's and map producer's accuracies. In addition, the stability with initial parameter conditions, classification error matrices, and error curves are analysed in some detail. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

I will investigate applications of machine learning algorithms to medical data, adaptations of differences in data collection, and the use of ensemble techniques. Focusing on the binary classification problem of Parkinson’s Disease (PD) diagnosis, I will apply machine learning algorithms to a primary dataset consisting of voice recordings from healthy and PD subjects. Specifically, I will use Artificial Neural Networks, Support Vector Machines, and an Ensemble Learning algorithm to reproduce results from [MS12] and [GM09]. Next, I will adapt a secondary regression dataset of PD recordings and combine it with the primary binary classification dataset, testing various techniques to consolidate the data including treating the regression data as unlabeled data in a semi-supervised learning approach. I will determine the performance of the above algorithms on this consolidated dataset. Performance of algorithms will be evaluated using 10-fold cross validation and results will be analyzed in a confusion matrix. Accuracy, precision, recall, and F-score will be calculated. The expands on past related work, which has used either a regression dataset alone to predict a Unified Parkinson’s Disease Rating Scale score for PD patients, or a classification dataset to determine healthy or PD diagnosis. In past work, the datasets have not been combined, and the regression set has not been used to contribute to evaluation of healthy subjects.

The representation of a signal using a learned dictionary instead of predefined operators, such as wavelets, has led to state-of-the-art results in various applications such as denoising, texture analysis, and face recognition. The area of dictionary learning is closely associated with sparse representation, which means that the signal is represented using few atoms in the dictionary. Despite recent advances in the computation of a dictionary using fast algorithms such as K-SVD, online learning, and cyclic coordinate descent, which make the computation of a dictionary from millions of data samples computationally feasible, the dictionary is mainly computed using unsupervised approaches such as k-means. These approaches learn the dictionary by minimizing the reconstruction error without taking into account the category information, which is not optimal in classification tasks. In this thesis, we propose a supervised dictionary learning (SDL) approach by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, the recently-introduced Hilbert Schmidt independence criterion (HSIC) is used. The learned dictionary is compact and has closed form; the proposed approach is fast. We show that it outperforms other unsupervised and supervised dictionary learning approaches in the literature on real-world data. Moreover, the proposed SDL approach has as its main advantage that it can be easily kernelized, particularly by incorporating a data-driven kernel such as a compression-based kernel, into the formulation. In this thesis, we propose a novel compression-based (dis)similarity measure. The proposed measure utilizes a 2D MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes on textures. Experimental results show that by incorporating the proposed measure as a kernel into our SDL, it significantly improves the performance of a supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures, as well as state-of-the-art SDL methods. It also improves the computation speed by about 40% compared to its closest rival. Eventually, we have extended the proposed SDL to multiview learning, where more than one representation is available on a dataset. We propose two different multiview approaches: one fusing the feature sets in the original space and then learning the dictionary and sparse coefficients on the fused set; and the other by learning one dictionary and the corresponding coefficients in each view separately, and then fusing the representations in the space of the dictionaries learned. We will show that the proposed multiview approaches benefit from the complementary information in multiple views, and investigate the relative performance of these approaches in the application of emotion recognition.

In this study, an urban growth model is used to simulate the urban growth in 2025 in the Antalya Metropolitan Area. It is the fastest growing metropolis in Turkey with a population growth of 41,79&amp / #8240 / , although Turkey&amp / #8217 / s growth is 18,28&amp / #8240 / for the last decade. An Urban Growth Model (SLEUTH, Version 3.0) is calibrated with cartographic data. The prediction is based on the archived data trends of the years of the 1987, 1996, and 2002 images, which are extracted from Landsat Thematic Mapper and Enhanced Thematic Mapper satellite images and the aerial photographs acquired in 1992 and the data are prepared to insert them as input into the model. The urban extent is obtained through supervised classification of the satellite images and visual interpretation of aerial photographs. The model calibration, where a predetermined order of stepping through the coefficient space is used is performed in order to determine the best fit values for the five growth control parameters including the coefficients of diffusion, breed and spread, slope and road gravity with the historical urban extent data. The development trend in Antalya is simulated by slowing down growth by taking into consideration the road development and environmental protection. After the simulation for a period of 23 years, 9824 ha increased in urban areas is obtained for 2025.

This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse waveforms obtained by the SphygmoCor"??" device is then analysed by the unsupervised learning technique of Self Organising Maps (SOM). The map patterns indicate that the observed arterial pressures affect the wave shape in a similar way as predicted by the theoretical model. A database of continuous arterial pressure obtained by catheter line during sleep is used to derive supervised models that enable estimation of arterial pressures, based on the measured wave shapes. Independent component analysis (ICA) is also used in a supervised learning methodology to show the theoretical plausibility of separating the pressure signals from unwanted noise components. The accuracy and repeatability of the SphygmoCor?? device is measured and discussed. Alternative regression models are introduced that improve on the existing models in the estimation of central cardiovascular parameters from peripheral arterial wave shapes. Results of this investigation show that from the information in the wave shape, it is possible, in theory, to estimate the continuous underlying pressures within the artery to a degree of accuracy acceptable to the Association for the Advancement of Medical Instrumentation. This could facilitate a new role for non-invasive sphygmographic devices, to be used not only for feature estimation but as alternatives to invasive arterial pressure sensors in the measurement of continuous blood pressure.

Thesis (Ph.D.) OGI School of Science & Engineering at OHSU, June 2008. / Includes bibliographical references (leaves 138-145).

Thesis (M.S.)--Oregon State University, 2004. / Printout. Includes bibliographical references (leaves 45-46). Also available on the World Wide Web.

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Supervised classifiers are the most popular approach for image classification due to their high accuracies, ease of use and strong theoretical grounding. Their primary disadvantage is the high level of user input required during the creation of the data needed to train the classifier. One alternative to supervised classification is an expert-system rule-based approach where expert knowledge is used to create a set of rules which can be applied to multiple images. This research compared supervised and expert-system rule-based approaches for forest mapping. For this purpose two SPOT 5 images were acquired and atmospherically corrected. Field visits, aerial photography, high resolution imagery and expert forestry knowledge were used for the compilation of the training data and the development of a rule-set. Both approaches were evaluated in an object-orientated environment. It was found that the accuracy of the resulting maps was equivalent, with both techniques returning an overall classification accuracy of 90%. This suggests that cost-effectiveness is the decisive factor for determining which method is superior. Although the development of the rule-set was time-consuming and challenging, it did not require any training data. In contrast, the supervised approach required a large number of training areas for each image classified, which was time-consuming and costly. Significantly more training areas will be required when the technique is applied to large areas, especially when multiple images are used. It was concluded that the rule-set is more cost-effective when applied at regional scale, but it is not viable for mapping small areas. / AFRIKAANSE OPSOMMING: Gerigte klassifiseerders is die gewildste benadering tot beeldklassifikasie as gevolg van hulle hoë graad van akkuraatheid, maklike aanwending en kragtige teoretiese fundering. Die primere nadeel van gerigte klassifikasie is die hoë vlak van gebruikersinsette wat benodig word tydens die skepping van opleidingsdata. 'n Alternatief vir gerigte klassifikasie is 'n deskundige stelsel waarin ‘n reëlgebaseerde benadering gevolg word om deskundige kennis aan te wend vir die opstel van 'n stel reëls wat op meervoudige beelde toegepas kan word. Hierdie navorsing het gerigte en deskundige stelsel benaderings toegepas vir bosboukartering om die twee benaderings met mekaar te vergelyk. Vir dié doel is twee SPOT 5 beelde verkry en atmosferies gekorrigeer. Veldbesoeke, lugfotografie, hoë-resolusie beelde en deskundige bosboukennis is aangewend om opleidingsdata saam te stel en die stel reëls te ontwikkel. Beide benaderings is in 'n objekgeoriënteerde omgewing beoordeel. Die akkuraatheidsvlakke van die resulterende kaarte was ewe hoog vir beide tegnieke met 'n algehele klassifikasie-akkuraatheid van 90%. Dit wil dus voorkom asof koste-effektiwiteit eerder as akkuraatheid die deurslaggewende faktor is om te bepaal watter metode die beste is. Alhoewel die ontwikkeling van die stel reëls tydrowend en uitdagend was, het dit geen opleidingsdata vereis nie. In teenstelling hiermee is 'n groot aantal opleidingsgebiede geskep vir elke beeld wat met gerigte klassifikasie verwerk is – 'n tydrowende en duur opsie. Dit is duidelik dat meer opleidingsgebiede benodig sal word wanneer die tegniek op groot gebiede toegepas word, veral omdat meervoudige beelde gebruik sal word. Gevolglik sal die stel reëls meer kosteeffektief wees wanneer dit op streekskaal toegepas word. ‘n Deskundige stelsel benadering is egter nie lewensvatbaar vir die kartering van klein gebiede nie.