Abstract
The study aims to explore how the mathematics teachers taking post-graduate education use their Algebra knowledge for the expression, “You can’t add apples and pears” that is expressed in almost every segment of the society, in creating mathematical content. The data of the research consisted of the written answers of the thirty-seven (37) teachers, who had taken the Algebra Teaching course, to the question “There is a rule in mathematics as everyone says; explain the statement ‘You can’t add apples and pears’ mathematically” in the exams from the 2008-2009 academic year to, including 2015-2016 academic year. The participants took the algebra education in the courses named Algebra and Introduction to Algebra courses and sufficient algebra education was provided to answer the question in the courses. The document analysis method was employed in the study and the data were analyzed with the descriptive content analysis. The results indicated that most of the teachers were aware of their algebra knowledge, the number and quality of the mathematical contents in the answers were at a satisfactory level, but the visuals used in some contents were not appropriate.
Highlights
Significant competing of the performance of line-based normalization model with other classical models gives a hint that the proposed model can offer a new alternative to classical normalization methods used in literature
During the implementation of line-based normalization, by taking into account that the feature vectors can be in different dimensions in a dataset, in the first stage the features have been made dimensionless and normalized afterwards
During the test of proposed method, besides the prediction dataset, classification dataset that are widely available in literature have been tried to observe possible results for classification dataset
Summary
Gerek tahmin gerekse sınıflama verilerinin mühendislik uygulamalarında özel bir önemi vardır. Bu bağlamda YSA oldukça popüler bir yapay zeka modelidir ve literatürde mühendislik, tıp gibi pek çok disiplinde çok sayıda çalışma vardır. Özellikle verileri yapay zekaya uygulamadan önce yapılacak olan bir ya da birkaç ön işleme; transfer fonksiyonu seçimi, normalizasyon, filtreleme gibi doğru adımlar hedefin yakalanmasında önemli katkı sağlayabilir (Beiu 1996, Minai 1993). Yapay zeka ile çözülmesi düşünülen problem bir tahmin verisi olmak zorunda değildir. Özellikle görüntü işleme gibi yoğun veri kümelerinde yapılan sınıflamalarda yapay zeka oldukça hızlı cevap verebilir ve bir tercih sebebi olabilir (Cios 1996). Özellikle dış dünyadan alınan tıbbi veri setleri oldukça karmaşık ve birçok parametreye bağlı sağlıklı-hasta ayrımı yapılan sınıflama veri kümelerine bir örnek teşkil edebilir (Güven 2005-2006-2008). Bu tür veriler yapay zeka uygulamalarında oldukça yaygın tercih edilen giri ile çıkışın kolayca sınıflama yapılamadığı veri kümleridir
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