This paper contains a brief description of a new computer programming tool for supervised machinelearning, designed to generate production rules from data. The research tool described - named NGTS - was used for pred...
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ISBN:
(纸本)9781424475605
This paper contains a brief description of a new computer programming tool for supervised machinelearning, designed to generate production rules from data. The research tool described - named NGTS - was used for prediction of the Glasgow Outcome Scale and Rankin Scale for patients affected by severe brain damage.
Medical datamining is so challenging. In this paper, we propose a new datamining algorithm called GAJA2, which is a derivation of GAJA [1]. We apply GAJA2 to mine Acute Inflammations data set, a medical data set got...
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Medical datamining is so challenging. In this paper, we propose a new datamining algorithm called GAJA2, which is a derivation of GAJA [1]. We apply GAJA2 to mine Acute Inflammations data set, a medical data set got from UCI machinelearning repository 2009[2]. This data set is about symptoms and diagnosis of two diseases of urinary system which are inflammation of urinary bladder and Nephritis of renal pelvis origin. The results show that knowledge mined by using GAJA2 is very interesting. We compare the results from GAJA2 with GAJA and Rough Set Theory. We found that the results from GAJA2 can be used by the experts in the fields and are very much easier to understand than from GAJA and Rough Set Theory.
Support Vector machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-writt...
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Support Vector machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector machine. It has several kernel functions including linear, polynomial and radial basis for performing classification. Our comparison between polynomial and radial basis kernel functions for selected feature conclude that radial basis function is preferable than polynomial for large datasets.
The two dimensional fractional Fourier transform (FrFT), which is a generalization of the Fourier transform, has many applications in several areas including signal processing and optics. Signal processing and pattern...
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The two dimensional fractional Fourier transform (FrFT), which is a generalization of the Fourier transform, has many applications in several areas including signal processing and optics. Signal processing and patternrecognition algorithms make extensive use of convolution. In patternrecognition, convolution is an important tool because of its translation invariance properties. Also convolution is a powerful way of characterizing the input-output relationship of time invariant linear system. In this paper the convolution theorem for two dimensional fractional Fourier transform in the generalized sense is proved.
Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against suc...
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Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. datamining and machinelearning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of datamining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on datamining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.
Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a learning Classifier System and data Minin...
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ISBN:
(纸本)9781424475605
Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a learning Classifier System and datamining Techniques. These techniques share the common factor of database dependence and there was actually little effort into making the system to understand the way human were behaving in a given time in the space. Using Artificial Intelligence techniques, we present a work that reads and classifies user object activity.
Risk Management is a logical and systematic method of identifying, analyzing, treating and monitoring the risks involved in any activity or process. The key to successful risk management lies in the ability to tailor ...
Risk Management is a logical and systematic method of identifying, analyzing, treating and monitoring the risks involved in any activity or process. The key to successful risk management lies in the ability to tailor a formal risk management process that addresses the complementary needs of the business and its customers. A formal risk management process is a continuous process for systematically addressing risk throughout the product/project life-cycle. Risks can be introduced (or latently reside) at the very earliest stages of the project life-cycle. The ability to identify risks earlier translates into earlier risk removal, at less cost, which promotes higher project success probability. datamining refers to discovery or “mining” of knowledge from large amounts of data. datamining has been described as a confluence of different disciplines primarily database systems, statistics, machinelearning and information science. This paper aims to study the conceptual mapping of Risk Management to datamining. A new paradigm has been suggested for Risk Management using the main attributes and key aspects of datamining.
Spectral clustering has been widely used in datamining in the past years. The performance of spectral clustering is very sensitive to the selection of scale parameter. Especially, when data has multi-scale it is very...
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Spectral clustering has been widely used in datamining in the past years. The performance of spectral clustering is very sensitive to the selection of scale parameter. Especially, when data has multi-scale it is very difficult to find a proper value for the scale parameter. To solve the problem, an improved method based on adaptive neighbor distance sort order has been proposed in this paper. The method enlarges the affinity between two points in the same cluster and reduces that in different clusters. Our experiments on the synthetic and real life datasets have shown promising results comparing with tradition method and k-means.
Most existing typical semi-supervised learning algorithms focused on the results of learning while facing the conflict on constraints. And most solutions use unsupervised distance-based methods to adjust the conflicti...
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Most existing typical semi-supervised learning algorithms focused on the results of learning while facing the conflict on constraints. And most solutions use unsupervised distance-based methods to adjust the conflicting constraints on the information by recalculating the samples' distance. This paper presents a constraint-based semi-supervised dimensionality reduction algorithm with conflict detection, called CDSSDR, which uses the information of priori constraints to adjust the contradictions in the constraints. It avoids the use of unsupervised methods to adjust the prior knowledge.
Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and...
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Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and sometimes triggered by random events which are considered false alarms. The intent of this project is to supplement human intelligence in a sensor network framework that can assist in filtering and real-time decision making from the large volume of data generated. Our conceptual design of a human-computer system is to use off-line learning to identify the important patterns. The critical real-time system uses the identified patterns from off-line learning in a system that relates the risks of false alarms with the length of patterns and the time interval distributions between sensors in the patterns to allow the human to generate intervention decisions. The human would supplement the computer information with the current threat levels and the available resources for reactions.
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