Browsing by Subject "Categorization"

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Categorization in a hierarchically structured text database
(2001) Kutlu, Ferhat
Over the past two decades there has been a huge increase in the amount of data being stored in databases and the on-line ﬂow of data by the effects of improvements in Internet. This huge increase brought out the needs for intelligent tools to manage that size of data and its ﬂow. Hierarchical approach is the best way to satisfy these needs and it is so widespread among people dealing with databases and Internet. Usenet newsgroups system is one of the on-line databases that have built-in hierarchical structures. Our point of departure is this hierarchical structure which makes categorization tasks easier and faster. In fact most of the search engines in Internet also exploit inherent hierarchy of Internet. Growing size of data makes most of the traditional categorization algorithms obsolete. Thus we developed a brand-new categorization learning algorithm which constructs an index tree out of Usenet news database and then decides the related newsgroups of a new news by categorizing it over the index tree. In learning phase it has an agglomerative and bottom-up hierarchical approach. In categorization phase it does an overlapping and supervised categorization. k Nearest Neighbor categorization algorithm is used to compare the complexity measure and accuracy of our algorithm. This comparison does not only mean comparing two different algorithms but also means comparing hierarchical approach vs. ﬂat approach, similarity measure vs. distance measure and importance of accuracy vs. importance of speed. Our algorithm prefers hierarchical approach and similarity measure, and greatly outperforms k Nearest Neighbor categorization algorithm in speed with minimal loss of accuracy.
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A systematic analysis of category learning: effects of different learning strategies and task characteristics
(2019-06) Solmaz, Elif Cemre
Many studies showed that organization of study materials has a strong effect on learning performance (Kornell & Bjork, 2008). The current study compared category learning performance through blocked and interleaved learning conditions, using verbal (Experiment 1) and pictorial (Experiment 2) materials. Participants were assigned to one of three conditions at encoding phase: blocked, interleaved, and semi-interleaved learning conditions. In blocked learning, participants studied four exemplars of the same category within the same trial, and categories were blocked across trials. In interleaved learning, participants studied one exemplar from four different categories within the same trial, and categories were interleaved across trials. In semi-interleaved learning, participants studied four exemplars of the same category within the trial, but categories were interleaved across trials. At testing phase, participants were tested on old and novel exemplars of the categories that they studied and were asked to identify the category of each exemplar. Lastly, they were tested on explicit understanding of categories. Both Experiment 1 and 2 revealed that participants produced the highest learning performance in the semi- interleaved learning condition. Learning similarities within the same trial and learning differences across trials might lead to the most optimal learning strategy for category learning, regardless of the type of stimuli used.