PMAPh_Kirke_AISB_final6
... rules can be used to compute. However music holds a unique place as a form of emotional expression [3]. Thus if the symbols and rules of music can be used in computations related to affective (emotional) processes, then the possibility arises of a symbol set which expresses the results of its own co ...
... rules can be used to compute. However music holds a unique place as a form of emotional expression [3]. Thus if the symbols and rules of music can be used in computations related to affective (emotional) processes, then the possibility arises of a symbol set which expresses the results of its own co ...
Automatic clustering with application to time dependent fault detection in chemical processes
... Gath-Geva). A novel approach to the clustering problem of time-series data is proposed. It exploits the time dependency of variables (time delays) within a process engineering environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optima ...
... Gath-Geva). A novel approach to the clustering problem of time-series data is proposed. It exploits the time dependency of variables (time delays) within a process engineering environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optima ...
Artificial Intelligence and Expert Systems in Mass Spectrometry
... but there are a multitude of both books and research articles related to this topic..14,15/ Today, many parts of our world are monitored, and in some cases controlled, by automated, computerized equipment. In an effort to make these devices more responsive and efficient, many of them employ embedded ...
... but there are a multitude of both books and research articles related to this topic..14,15/ Today, many parts of our world are monitored, and in some cases controlled, by automated, computerized equipment. In an effort to make these devices more responsive and efficient, many of them employ embedded ...
Inductive Intrusion Detection in Flow-Based
... attribute is eliminated until the algorithm ended up with B. . The basic functionality of genetic algorithms as described in [88, p. 1]. An initial random population is created out of the input features. Then, mutation and/or crossover is performed as long as the cost function does not decide that t ...
... attribute is eliminated until the algorithm ended up with B. . The basic functionality of genetic algorithms as described in [88, p. 1]. An initial random population is created out of the input features. Then, mutation and/or crossover is performed as long as the cost function does not decide that t ...
portable document (.pdf) format
... iterative procedure for structural equation models. Succinctly, starting values for the estimates are declared and succeeding iterations are based on the first two Taylor polynomial expansions leading to subsequent estimates that are hopefully closer and closer to the maximized solution for the esti ...
... iterative procedure for structural equation models. Succinctly, starting values for the estimates are declared and succeeding iterations are based on the first two Taylor polynomial expansions leading to subsequent estimates that are hopefully closer and closer to the maximized solution for the esti ...
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
... to 1. The main focus of all learning algorithms is to improve the recall, without sacrificing the precision. However, the recall and precision goals are often conflicting and attacking them simultaneously may not work well, especially when one class is rare. The F-value incorporates both precision a ...
... to 1. The main focus of all learning algorithms is to improve the recall, without sacrificing the precision. However, the recall and precision goals are often conflicting and attacking them simultaneously may not work well, especially when one class is rare. The F-value incorporates both precision a ...
Pareto-Based Multiobjective Machine Learning: An
... or classification. Unsupervised learning belongs to the second category of learning algorithms. Data clustering is a typical unsupervised learning method, where a given set of data is to be assigned to different subsets (clusters) so that the data in each subset share some common trait (similarity) ...
... or classification. Unsupervised learning belongs to the second category of learning algorithms. Data clustering is a typical unsupervised learning method, where a given set of data is to be assigned to different subsets (clusters) so that the data in each subset share some common trait (similarity) ...
WWW-newsgroup-document Clustering by Means of
... Figs. 1 and 2 present the performance of the proposed clustering technique for 20 newsgroups”Small” and ”Large” numerical models of 20 newsgroups collection of documents. As the learning progresses, both systems adjust the overall numbers of neurons in their networks (Figs. 1a and 2a) that finally a ...
... Figs. 1 and 2 present the performance of the proposed clustering technique for 20 newsgroups”Small” and ”Large” numerical models of 20 newsgroups collection of documents. As the learning progresses, both systems adjust the overall numbers of neurons in their networks (Figs. 1a and 2a) that finally a ...
Noise Tolerant Data Mining
... Existing research efforts (Maletic and Marcus 2000; Orr 1998) have suggested that the average error rate of a dataset in a data mining application is around 5%-10%. There are numerous reasons that contribute to data imperfections. For instance, faulty measuring devices, transcription errors, and tra ...
... Existing research efforts (Maletic and Marcus 2000; Orr 1998) have suggested that the average error rate of a dataset in a data mining application is around 5%-10%. There are numerous reasons that contribute to data imperfections. For instance, faulty measuring devices, transcription errors, and tra ...
The Promise of Artificial Intelligence
... AI is a field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. As the Association for the Advancement of Artificial Intelligence describes it, AI is “the scientific understanding of the mechanisms underlyi ...
... AI is a field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. As the Association for the Advancement of Artificial Intelligence describes it, AI is “the scientific understanding of the mechanisms underlyi ...
Japan`s strategies for taking the lead in the Fourth Industrial
... Two scenarios of Forth Industrial Revolution -Japan now at “turning point"[As-is scenario] - Retaining industrial/employment structure ...
... Two scenarios of Forth Industrial Revolution -Japan now at “turning point"[As-is scenario] - Retaining industrial/employment structure ...
A Closest Fit Approach to Missing Attribute Values in Preterm Birth
... given case with a missing attribute value, we may look for the closest fitting cases within the same concept, as defined by the case with missing attribute value, or in all concepts, i.e., among all cases. The former algorithm is called concept closest fit, the latter is called global closest fit. S ...
... given case with a missing attribute value, we may look for the closest fitting cases within the same concept, as defined by the case with missing attribute value, or in all concepts, i.e., among all cases. The former algorithm is called concept closest fit, the latter is called global closest fit. S ...
Machine Condition Monitoring Using Artificial Intelligence: The
... [3]. Condition monitoring approaches have produced considerable savings by reducing unplanned outage of machinery, reducing downtime for repair and improving reliability and safety. Condition monitoring is a technique of sensing equipment health; operating information and analyzing this information ...
... [3]. Condition monitoring approaches have produced considerable savings by reducing unplanned outage of machinery, reducing downtime for repair and improving reliability and safety. Condition monitoring is a technique of sensing equipment health; operating information and analyzing this information ...
Yarn tenacity modeling using artificial neural networks and
... determine the values of input materials only by “trial and error” and their former experiences. Investigations show that although there is many research findings focused on modeling of tenacity but only a few of them proposed a practical approach to optimal determination of values for effective inpu ...
... determine the values of input materials only by “trial and error” and their former experiences. Investigations show that although there is many research findings focused on modeling of tenacity but only a few of them proposed a practical approach to optimal determination of values for effective inpu ...
1 Copyright © 2009 Pearson Education, Inc. Publishing as Prentice
... Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall ...
... Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall ...
A New Approach to Classification with the Least Number of Features
... k ∗ = 512 to k ∗ = 4096 is well below the standard deviation. Table II compares the capability of both variable selection methods to identify relevant and irrelevant features. Obviously, the SFM returns a lower number of features which are more likely to be truly relevant features. Even in highdimen ...
... k ∗ = 512 to k ∗ = 4096 is well below the standard deviation. Table II compares the capability of both variable selection methods to identify relevant and irrelevant features. Obviously, the SFM returns a lower number of features which are more likely to be truly relevant features. Even in highdimen ...
Semi-supervised collaborative clustering with partial background
... a new kind of algorithms has been investigated during the last ten years under the names of semi-supervised classification [14] (or semi-supervised learning [22]) and semisupervised clustering [1]. In semi-supervised classification, the training step uses unlabeled samples in addition to the availab ...
... a new kind of algorithms has been investigated during the last ten years under the names of semi-supervised classification [14] (or semi-supervised learning [22]) and semisupervised clustering [1]. In semi-supervised classification, the training step uses unlabeled samples in addition to the availab ...
comparison of purity and entropy of k-means
... Clustering is the one of the vital areas in data mining. The evaluation of the performance of the clustering algorithm, we have to use the validation measures. There are two types of validation measures; they are internal validation measures and external validation measures. The internal validation ...
... Clustering is the one of the vital areas in data mining. The evaluation of the performance of the clustering algorithm, we have to use the validation measures. There are two types of validation measures; they are internal validation measures and external validation measures. The internal validation ...
Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms
... Kurgan, 2001). 1.2. Generation of Hypotheses In the machine learning world one works with data that represent information. The definition of an information system is given below: IS =< S , Q, V , f > ...
... Kurgan, 2001). 1.2. Generation of Hypotheses In the machine learning world one works with data that represent information. The definition of an information system is given below: IS =< S , Q, V , f > ...
Artificial Intelligence Meets the Mainstream
... Zuckerberg showcased Jarvis in a video on his Facebook page.1 The video revealed that his proof-of-concept actually worked. With just a few glitches — the AI, voiced by Morgan Freeman, played music, dimmed lights, toasted bread, operated a t-shirt cannon, and even made jokes. Of course, this is not ...
... Zuckerberg showcased Jarvis in a video on his Facebook page.1 The video revealed that his proof-of-concept actually worked. With just a few glitches — the AI, voiced by Morgan Freeman, played music, dimmed lights, toasted bread, operated a t-shirt cannon, and even made jokes. Of course, this is not ...
Improving the Knowledge-Based Expert System Lifecycle
... Traditional knowledge-based expert system implementations do not utilize the information contained in these historical data repositories even though these repositories contain valuable information that could be mined into the knowledge base for future use. 1.3 Issues Associated with Knowledge Base M ...
... Traditional knowledge-based expert system implementations do not utilize the information contained in these historical data repositories even though these repositories contain valuable information that could be mined into the knowledge base for future use. 1.3 Issues Associated with Knowledge Base M ...
Feature Subset Selection - Department of Computer Science
... in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner (IB1). Experiments using ...
... in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner (IB1). Experiments using ...
Trace optimization and eigenproblems
... already deciphered digits available in a given data set. In order to perform these tasks it is common to first process the given datasets (e.g. the database of handwritten digits) in order to reduce its dimension, i.e., to find a dataset of much lower dimension than the original one but which preser ...
... already deciphered digits available in a given data set. In order to perform these tasks it is common to first process the given datasets (e.g. the database of handwritten digits) in order to reduce its dimension, i.e., to find a dataset of much lower dimension than the original one but which preser ...
NLP Biosurveillance-Interface2004-chapman2
... Can we accurately identify variables from text? • Does measure NLP application’s ability to identify findings, ...
... Can we accurately identify variables from text? • Does measure NLP application’s ability to identify findings, ...
Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and
... interval value as well as crisp value, of which nominal attributes are represented by crisp nominal value, and of which class has confidence factor. It also presents an experiment result to show the applicability of the proposed method. ...
... interval value as well as crisp value, of which nominal attributes are represented by crisp nominal value, and of which class has confidence factor. It also presents an experiment result to show the applicability of the proposed method. ...