• Title, Summary, Keyword: Category Representation Algorithm

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A Study of Designing the Intelligent Information Retrieval System by Automatic Classification Algorithm (자동분류 알고리즘을 이용한 지능형 정보검색시스템 구축에 관한 연구)

  • Seo, Whee
    • Journal of Korean Library and Information Science Society
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    • v.39 no.4
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    • pp.283-304
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    • 2008
  • This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

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Teaching and Learning Irrational Number with Its Conceptual Aspects Stressed : Consideration of Irrational Number through the Conception of 'Incommensurability' (무리수의 개념적 측면을 강조한 교육방안: '통약불가능성'을 통한 무리수 고찰)

  • 변희현;박선용
    • School Mathematics
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    • v.4 no.4
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    • pp.643-655
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    • 2002
  • In this paper we emphasize the introduction of ‘incommensurability’ on the teaching and learning the irrational number because we think of the origin of number as ‘ratio’. According to Greek classification of continuity as a ‘never ending’ divisibility, discrete number and continuous magnitude belong to another classes. That is, those components were dealt with respectively in category of arithmetic and that of geometry. But the comparison between magnitudes in terms of their ratios took the opportunity to relate ratios of magnitudes with numerical ratios. And at last Stevin coped with discrete and continuous quantity at the same time, using his instrumental decimal notation. We pay attention to the fact that Stevin constructed his number conception in reflecting the practice of measurement : He substituted ‘subdivision of units’ for ‘divisibility of quantities’. Number was the result of such a reflective abstraction. In other words, number was invented by regulation of measurement. Therefore, we suggest decimal representation from the point of measurement, considering the foregoing historical development of number. From the perspective that the conception of real number originated from measurement of ‘continuum’ and infinite decimals played a significant role in the ‘representation’ of measurement, decimal expression of real number should be introduced through contexts of measurement instead of being introduced as a result of algorithm.

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The Churchlands' Theory of Representation and the Semantics (처칠랜드의 표상이론과 의미론적 유사성)

  • Park, Je-Youn
    • Korean Journal of Cognitive Science
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    • v.23 no.2
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    • pp.133-164
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    • 2012
  • Paul Churchland(1989) suggests the theory of representation from the results of cognitive biology and connectionist AI studies. According to the theory, our representations of the diverse phenomena in the world can be represented as the positions of phase state spaces with the actions of the neurons or of the assembly of neurons. He insists connectionist AI neural networks can have the semantical category systems to recognize the world. But Fodor and Lepore(1996) don't look the perspective bright. From their points of view, the Churchland's theory of representation stands on the base of Quine's holism, and the network semantics cannot explain how the criteria of semantical content similarity could be possible, and so cannot the theory. This thesis aims to excavate which one is the better between the perspective of the theory and the one of Fodor and Lepore's. From my understandings of state space theory of representation, artificial nets can coordinates the criteria of contents similarity by the learning algorithm. On the basis of these, I can see that Fodor and Lepore's points cannot penetrate the Churchlands' theory. From the view point of the theory, we can see how the future's artificial systems can have the conceptual systems recognizing the world. Therefore we can have the perspectives what cognitive scientists have to focus on.

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The Pattern Analysis of Financial Distress for Non-audited Firms using Data Mining (데이터마이닝 기법을 활용한 비외감기업의 부실화 유형 분석)

  • Lee, Su Hyun;Park, Jung Min;Lee, Hyoung Yong
    • Journal of Intelligence and Information Systems
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    • v.21 no.4
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    • pp.111-131
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    • 2015
  • There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.