Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226799
S. Chi, Wei-ling Peng, Pei-Tsang Wu, Mingtao Yu
The purpose of this research is to study the relationship of changes between the stock indicators and stock index in order to understand how the trend of stock index change is under the complex influence among the stock technical indicators. The proposed methodology, first of all, applies the self-organizing map (SOM) neural network to cluster the similar indicators into groups based on their similarity of moving curve within a certain period of time. To investigate the relationship between the stock index and the technical indicators within any of the groups, the fuzzy neural network (FNN) technique is employed to search for the rules about their relationships. To evaluate the performance of the SOM, the grey relationship analysis was used for the verification of how similar of the indicators which was clustered into a group. According to the results, it is clear that the capability of the SOM in clustering is confirmed. To further improve the predication accuracy, this research selected some key indicators from each of the groups as the inputs of neural network and the results completes a much better prediction accuracy than all of the previous networks.
{"title":"The study on the relationship among technical indicators and the development of stock index prediction system","authors":"S. Chi, Wei-ling Peng, Pei-Tsang Wu, Mingtao Yu","doi":"10.1109/NAFIPS.2003.1226799","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226799","url":null,"abstract":"The purpose of this research is to study the relationship of changes between the stock indicators and stock index in order to understand how the trend of stock index change is under the complex influence among the stock technical indicators. The proposed methodology, first of all, applies the self-organizing map (SOM) neural network to cluster the similar indicators into groups based on their similarity of moving curve within a certain period of time. To investigate the relationship between the stock index and the technical indicators within any of the groups, the fuzzy neural network (FNN) technique is employed to search for the rules about their relationships. To evaluate the performance of the SOM, the grey relationship analysis was used for the verification of how similar of the indicators which was clustered into a group. According to the results, it is clear that the capability of the SOM in clustering is confirmed. To further improve the predication accuracy, this research selected some key indicators from each of the groups as the inputs of neural network and the results completes a much better prediction accuracy than all of the previous networks.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124580983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226818
V. Kreinovich, Praveen Patangay, L. Longpré, S. Starks, Cynthia Campos
In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some "normal" values x/sub 1/,..., x/sub n/, compute the sample average E, the sample standard variation /spl sigma/, and then mark a value x as an outlier if x is outside the k/sub 0/-sigma interval [E-k/sub 0//spl middot//spl sigma/, E+k/sub 0//spl middot//spl sigma/] (for some pre-selected parameter k/sub 0/). In real life, we often have only interval ranges [x/sub i/, x~/sub i/] for the normal values x/sub 1/,...,x/sub n/. In this case, we only have intervals of possible values for the bounds E-k/sub 0//spl middot//spl sigma/ and E+k/sub 0//spl middot//spl sigma/. We can therefore identify outliers as values that are outside all k/sub 0/-sigma intervals. In this paper, we analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions). We also provide algorithms that estimate the degree of "outlier-ness" of a given value x-measured as the largest value k/sub 0/ for which x is outside the corresponding k/sub 0/-sigma interval.
{"title":"Outlier detection under interval and fuzzy uncertainty: algorithmic solvability and computational complexity","authors":"V. Kreinovich, Praveen Patangay, L. Longpré, S. Starks, Cynthia Campos","doi":"10.1109/NAFIPS.2003.1226818","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226818","url":null,"abstract":"In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some \"normal\" values x/sub 1/,..., x/sub n/, compute the sample average E, the sample standard variation /spl sigma/, and then mark a value x as an outlier if x is outside the k/sub 0/-sigma interval [E-k/sub 0//spl middot//spl sigma/, E+k/sub 0//spl middot//spl sigma/] (for some pre-selected parameter k/sub 0/). In real life, we often have only interval ranges [x/sub i/, x~/sub i/] for the normal values x/sub 1/,...,x/sub n/. In this case, we only have intervals of possible values for the bounds E-k/sub 0//spl middot//spl sigma/ and E+k/sub 0//spl middot//spl sigma/. We can therefore identify outliers as values that are outside all k/sub 0/-sigma intervals. In this paper, we analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions). We also provide algorithms that estimate the degree of \"outlier-ness\" of a given value x-measured as the largest value k/sub 0/ for which x is outside the corresponding k/sub 0/-sigma interval.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132853699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226797
Zhou Zhuan, Ding Xiangqian, Liu Wenbin
In this paper, the concepts of fuzzy traffic network, road width and maximum road are introduced. Based on the fuzzy matrix representing traffic network the connection between the maximum road width and the power operation of the fuzzy matrix is discussed. And then the arithmetic solving method and the implemental program for the maximum road are introduced. They have practical value in analyzing traffic network such as regulating the flow of traffic in cities.
{"title":"Fuzzy matrix analysis of the maximum road in traffic network","authors":"Zhou Zhuan, Ding Xiangqian, Liu Wenbin","doi":"10.1109/NAFIPS.2003.1226797","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226797","url":null,"abstract":"In this paper, the concepts of fuzzy traffic network, road width and maximum road are introduced. Based on the fuzzy matrix representing traffic network the connection between the maximum road width and the power operation of the fuzzy matrix is discussed. And then the arithmetic solving method and the implemental program for the maximum road are introduced. They have practical value in analyzing traffic network such as regulating the flow of traffic in cities.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226822
P. Melin, E. Zamudio, O. Castillo
In this paper is proposed a proposed control system for optimising the transmission power in cellular phones. As the mobile station moves toward the edge of a cell, the cell's base station notes that the signal strength is diminishing. Meanwhile, the base station in the cell the mobile station is moving toward (which is listening and measuring signal strength on all frequencies, not just its own one) sees the phone's signal strength increasing. The two base stations coordinate with each other, and at some point, the phone gets a signal on a control channel telling it to change frequencies. This hand off switches the phone to a different cell, which receives the signal with a bigger intensity, so the next decrement of the transmission power will be the lower possible without risking the quality of the transmission. Nowadays the central cellular controls the transmission power on the mobile station, on intervals of 4 dbs to increase or decrease it, so the final power always is above or under the required power.
{"title":"Intelligent control of the transmission power in cellular phones using fuzzy logic","authors":"P. Melin, E. Zamudio, O. Castillo","doi":"10.1109/NAFIPS.2003.1226822","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226822","url":null,"abstract":"In this paper is proposed a proposed control system for optimising the transmission power in cellular phones. As the mobile station moves toward the edge of a cell, the cell's base station notes that the signal strength is diminishing. Meanwhile, the base station in the cell the mobile station is moving toward (which is listening and measuring signal strength on all frequencies, not just its own one) sees the phone's signal strength increasing. The two base stations coordinate with each other, and at some point, the phone gets a signal on a control channel telling it to change frequencies. This hand off switches the phone to a different cell, which receives the signal with a bigger intensity, so the next decrement of the transmission power will be the lower possible without risking the quality of the transmission. Nowadays the central cellular controls the transmission power on the mobile station, on intervals of 4 dbs to increase or decrease it, so the final power always is above or under the required power.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226783
S. Colak, C. Isik
Classification of systolic, mean and diastolic blood pressure profiles using the oscillometric method is a difficult process. Generally, the algorithms aim at extracting some parameters such as height, and ratios of the pulses at certain pressure levels, which are obtained from the cuff pressure. These parameters can be used to form profiles to relate to blood pressures. The effectiveness of the classification depends on many factors, such as environmental noise, white coat effect, heart rate variability and motion artifacts. In this paper, we investigate the effectiveness of a neuro-fuzzy approach to blood pressure classification. We employ feature extraction using principal component analysis, and fuzzy sets to classify pressure profiles.
{"title":"Fuzzy oscillometric blood pressure classification","authors":"S. Colak, C. Isik","doi":"10.1109/NAFIPS.2003.1226783","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226783","url":null,"abstract":"Classification of systolic, mean and diastolic blood pressure profiles using the oscillometric method is a difficult process. Generally, the algorithms aim at extracting some parameters such as height, and ratios of the pulses at certain pressure levels, which are obtained from the cuff pressure. These parameters can be used to form profiles to relate to blood pressures. The effectiveness of the classification depends on many factors, such as environmental noise, white coat effect, heart rate variability and motion artifacts. In this paper, we investigate the effectiveness of a neuro-fuzzy approach to blood pressure classification. We employ feature extraction using principal component analysis, and fuzzy sets to classify pressure profiles.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117306434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226804
M. Akbarzadeh-T., H. Rezaei-S, M. Naghibi-S
Cooperative learning in multi-agent systems is generally expected to improve both quality and speed of learning. This is particularly true when agents are able to recognize expert agents amongst themselves and integrate their knowledge properly. Additionally, the process of learning can be improved when the reinforcement learning signals in each agent can balance between searching behavior of the unknown knowledge (exploration) and learning behavior of the obtained knowledge (exploitation). In this paper, a fuzzy dynamic cooperative learning method, based on weighted strategy sharing (WSS), is introduced which draws a balance between exploitation and exploration behaviors. In the weighed strategy sharing method, agents share their learned knowledge by a measure of their expertness. The strategy, when applied to the classic herding problem, shows further improvement in quality and speed of learning when parameters of the learning algorithm are dynamically determined by a fuzzy routine.
{"title":"A fuzzy adaptive algorithm for expertness based cooperative learning, application to herding problem","authors":"M. Akbarzadeh-T., H. Rezaei-S, M. Naghibi-S","doi":"10.1109/NAFIPS.2003.1226804","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226804","url":null,"abstract":"Cooperative learning in multi-agent systems is generally expected to improve both quality and speed of learning. This is particularly true when agents are able to recognize expert agents amongst themselves and integrate their knowledge properly. Additionally, the process of learning can be improved when the reinforcement learning signals in each agent can balance between searching behavior of the unknown knowledge (exploration) and learning behavior of the obtained knowledge (exploitation). In this paper, a fuzzy dynamic cooperative learning method, based on weighted strategy sharing (WSS), is introduced which draws a balance between exploitation and exploration behaviors. In the weighed strategy sharing method, agents share their learned knowledge by a measure of their expertness. The strategy, when applied to the classic herding problem, shows further improvement in quality and speed of learning when parameters of the learning algorithm are dynamically determined by a fuzzy routine.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"32 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116098223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226767
S. Lancaster, M. J. Wierman
The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge. Conventional FLCs use Center of Gravity or Mean of Maxima defuzzification methods, though other methods have been studied. This paper compares the efficiency of many different models of the defuzzification process. The goal is to examine the accuracy of the output data and the amount of processing time required. A simple controller that backs a truck up to a gate is used in the study. All of the variables are granulated with trapezoidal fuzzy numbers. Some of the defuzzification methods examined are Fast Center of Gravity, Mean of Maxima, True Center of Gravity and various new methods that have shown promise in application.
{"title":"Empirical study of defuzzification","authors":"S. Lancaster, M. J. Wierman","doi":"10.1109/NAFIPS.2003.1226767","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226767","url":null,"abstract":"The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge. Conventional FLCs use Center of Gravity or Mean of Maxima defuzzification methods, though other methods have been studied. This paper compares the efficiency of many different models of the defuzzification process. The goal is to examine the accuracy of the output data and the amount of processing time required. A simple controller that backs a truck up to a gate is used in the study. All of the variables are granulated with trapezoidal fuzzy numbers. Some of the defuzzification methods examined are Fast Center of Gravity, Mean of Maxima, True Center of Gravity and various new methods that have shown promise in application.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226823
K. Balasubramanian, K. Rattan
A linearizing control scheme for a highly nonlinear pneumatic muscle (PM) system is presented in this paper. Linearizing controllers have been widely used in the control of robotic systems. Since PM is a highly nonlinear system, the concept of linearizing control can be extended to the control of these muscles. Pneumatic muscle has air pressure as its input and the output is a displacement of the muscle. The system is considered to be a mass-spring-damper system with a nonlinear damper and a spring. This nonlinearity makes the design of a mathematical controller more difficult. The scheme presented in this paper uses fuzzy logic to implement the controller. The controller has a model-based portion and a servo-based portion. The model-based portion cancels all the nonlinearities caused by the nonlinear damper and spring. Therefore, the system as seen by the servo-based portion is linear, which can then be controlled using a linear PID controller. The controller is conceptually simple but exhibited superior tracking capability.
{"title":"Fuzzy logic control of a pneumatic muscle system using a linearing control scheme","authors":"K. Balasubramanian, K. Rattan","doi":"10.1109/NAFIPS.2003.1226823","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226823","url":null,"abstract":"A linearizing control scheme for a highly nonlinear pneumatic muscle (PM) system is presented in this paper. Linearizing controllers have been widely used in the control of robotic systems. Since PM is a highly nonlinear system, the concept of linearizing control can be extended to the control of these muscles. Pneumatic muscle has air pressure as its input and the output is a displacement of the muscle. The system is considered to be a mass-spring-damper system with a nonlinear damper and a spring. This nonlinearity makes the design of a mathematical controller more difficult. The scheme presented in this paper uses fuzzy logic to implement the controller. The controller has a model-based portion and a servo-based portion. The model-based portion cancels all the nonlinearities caused by the nonlinear damper and spring. Therefore, the system as seen by the servo-based portion is linear, which can then be controlled using a linear PID controller. The controller is conceptually simple but exhibited superior tracking capability.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129589947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226812
C. Campos, G. R. Keller, V. Kreinovich, L. Longpré, François Modave, S. Starks, R. Torres
Geospatial databases generally consist of measurements related to points (or pixels in the case of raster data), lines, and polygons. In recent years, the size and complexity of these databases have increased significantly and they often contain duplicate records, i.e., two or more close records representing the same measurement result. In this paper, we use fuzzy measures to address the problem of detecting duplicates in a database consisting of point measurements. As a test case, we use a database of measurements of anomalies in the Earth's gravity field that we have compiled. We show that a natural duplicate deletion algorithm requires (in the worst case) quadratic time, and we propose a new asymptotically optimal O(n/spl middot/log(n)) algorithm. These algorithms have been successfully applied to gravity databases. We believe that they will prove to be useful when dealing with many other types of point data.
{"title":"The use of fuzzy measures as a data fusion tool in geographic information systems: case study","authors":"C. Campos, G. R. Keller, V. Kreinovich, L. Longpré, François Modave, S. Starks, R. Torres","doi":"10.1109/NAFIPS.2003.1226812","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226812","url":null,"abstract":"Geospatial databases generally consist of measurements related to points (or pixels in the case of raster data), lines, and polygons. In recent years, the size and complexity of these databases have increased significantly and they often contain duplicate records, i.e., two or more close records representing the same measurement result. In this paper, we use fuzzy measures to address the problem of detecting duplicates in a database consisting of point measurements. As a test case, we use a database of measurements of anomalies in the Earth's gravity field that we have compiled. We show that a natural duplicate deletion algorithm requires (in the worst case) quadratic time, and we propose a new asymptotically optimal O(n/spl middot/log(n)) algorithm. These algorithms have been successfully applied to gravity databases. We believe that they will prove to be useful when dealing with many other types of point data.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226801
John L. Mill, A. Inoue
Support Vector Machines (SVMs) are a recently introduced Machine Learning technique. SVMs approach binary classification by attempting to find a hyperplane that separates the two categories of training vectors. This hyperplane is expressed as a function of a subset of the training vectors. These vectors are called support vectors. In this paper, we present a method of fuzzifying support vectors based off of the results of an SVM induction. We then propose a method of enhancing SVM induction using these fuzzy support vectors. We finish by presenting a computational example using the IRIS data set.
{"title":"An application of fuzzy support vectors","authors":"John L. Mill, A. Inoue","doi":"10.1109/NAFIPS.2003.1226801","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226801","url":null,"abstract":"Support Vector Machines (SVMs) are a recently introduced Machine Learning technique. SVMs approach binary classification by attempting to find a hyperplane that separates the two categories of training vectors. This hyperplane is expressed as a function of a subset of the training vectors. These vectors are called support vectors. In this paper, we present a method of fuzzifying support vectors based off of the results of an SVM induction. We then propose a method of enhancing SVM induction using these fuzzy support vectors. We finish by presenting a computational example using the IRIS data set.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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