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Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management
Published on Oct 08, 20076930 Views
In this paper we explore the use of Tree Augmented Naive Bayes (TAN) in regression problems where some of the independent variables are continuous and some others are discrete. The proposed solution i
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Chapter list
Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management00:00
Outline pt 101:52
Outline pt 202:06
Outline pt 302:14
Outline pt 402:26
Outline pt 502:44
Outline pt 602:57
Motivation pt 103:00
Motivation pt 203:21
Motivation pt 304:17
Motivation pt 405:09
Bayesian Networks pt 105:30
Bayesian Networks pt 206:17
Bayesian Networks for Classification pt 106:46
Bayesian Networks for Classification pt 206:57
Bayesian Networks for Classification pt 307:43
Bayesian Networks for Classification pt 407:53
Bayesian Networks for Classification pt 508:03
Bayesian Networks for Classification pt 608:09
Bayesian Networks for Classification pt 708:32
Bayesian Networks for Regression pt 108:55
Bayesian Networks for Regression pt 209:50
The Model Proposed by Frank et al. pt 110:32
The Model Proposed by Frank et al. pt 210:40
The Model Proposed by Frank et al. pt 310:44
The Model Proposed by Frank et al. pt 410:53
The Model Proposed by Frank et al. pt 510:54
The Model Proposed by Morales et al. pt 111:29
The Model Proposed by Morales et al. pt 211:30
The Model Proposed by Morales et al. pt 311:39
The Model Proposed by Morales et al. pt 411:43
The MTE Model pt 111:50
The MTE Model pt 212:38
The MTE Model pt 312:52
Increasing the Structural Complexity: TAN Models13:48
Constructing a TAN Regression Model pt 114:33
Constructing a TAN Regression Model pt 214:50
Constructing a TAN Regression Model pt 315:08
Constructing a TAN Regression Model pt 415:19
Constructing a TAN Regression Model pt 515:29
Constructing a TAN Regression Model pt 615:31
Computing the Conditional Mutual Information pt 115:43
Computing the Conditional Mutual Information pt 215:58
Computing the Conditional Mutual Information pt 316:04
Case Studies pt 116:38
Case Studies pt 217:26
Case Studies pt 317:36
Case Studies pt 417:52
Case Studies pt 517:54
Results pt 118:21
Results pt 218:22
Conclusions pt 119:00
Conclusions pt 219:01
Conclusions pt 319:03
Conclusions pt 419:04
Conclusions pt 519:05
Conclusions pt 619:06