Slideshows by User: roywwcheng http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows by User: roywwcheng http://www.slideshare.net/ Thu, 05 Nov 2009 23:30:37 GMT SlideShare feed for Slideshows by User: roywwcheng Combining Instance-Based Learning and Logistic Regression for Multilabel Classi*cation http://www.slideshare.net/roywwcheng/combining-instancebased-learning-and-logistic-regression-for-multilabel-classication
Multilabel classification is an extension of conventional classifi cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and inter-dependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classifi cation, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture inter-dependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.]]>
Multilabel classification is an extension of conventional classifi cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and inter-dependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classifi cation, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture inter-dependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.]]> Thu, 05 Nov 2009 23:30:37 GMT http://www.slideshare.net/roywwcheng/combining-instancebased-learning-and-logistic-regression-for-multilabel-classication roywwcheng@slideshare.net(roywwcheng) Combining Instance-Based Learning and Logistic Regression for Multilabel Classi*cation roywwcheng Multilabel classification is an extension of conventional classifi*cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and inter-dependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classifi*cation, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture inter-dependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction. <img src="http://cdn.slidesharecdn.com/cheng-ecml09slides-091105173054-phpapp01-thumbnail-2?1257464782" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Multilabel classification is an extension of conventional classifi*cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and inter-dependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classifi*cation, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture inter-dependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction. Combining Instance-Based Learning and Logistic Regression for Multilabel Classi cation
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]]> 66 0 http://cdn.slidesharecdn.com/cheng-ecml09slides-091105173054-phpapp01-thumbnail-2?1257464782 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Label Ranking with Partial Abstention using Ensemble Learning http://www.slideshare.net/roywwcheng/label-ranking-with-partial-abstention-using-ensemble-learning
In label ranking, the problem is to learn a mapping from instances to rankings over a finite set of predefined class labels. In this paper, we consider a generalization of this problem, namely label ranking with a reject option. Just like in conventional classification, where a classifier can refuse a presumably unreliable prediction, the idea is to concede a label ranker the possibility to abstain. More specifically, the label ranker is allowed to make a weaker prediction in the form of a partial instead of a total order. Thus, unlike a conventional classifier which either makes a prediction or not, a label ranker can abstain to a certain degree. To realize label ranking with a reject option, we propose a method based on ensemble learning techniques. First empirical results are presented showing great promise for the usefulness of the approach.]]>
In label ranking, the problem is to learn a mapping from instances to rankings over a finite set of predefined class labels. In this paper, we consider a generalization of this problem, namely label ranking with a reject option. Just like in conventional classification, where a classifier can refuse a presumably unreliable prediction, the idea is to concede a label ranker the possibility to abstain. More specifically, the label ranker is allowed to make a weaker prediction in the form of a partial instead of a total order. Thus, unlike a conventional classifier which either makes a prediction or not, a label ranker can abstain to a certain degree. To realize label ranking with a reject option, we propose a method based on ensemble learning techniques. First empirical results are presented showing great promise for the usefulness of the approach.]]> Thu, 05 Nov 2009 23:28:08 GMT http://www.slideshare.net/roywwcheng/label-ranking-with-partial-abstention-using-ensemble-learning roywwcheng@slideshare.net(roywwcheng) Label Ranking with Partial Abstention using Ensemble Learning roywwcheng In label ranking, the problem is to learn a mapping from instances to rankings over a finite set of predefined class labels. In this paper, we consider a generalization of this problem, namely label ranking with a reject option. Just like in conventional classification, where a classifier can refuse a presumably unreliable prediction, the idea is to concede a label ranker the possibility to abstain. More specifically, the label ranker is allowed to make a weaker prediction in the form of a partial instead of a total order. Thus, unlike a conventional classifier which either makes a prediction or not, a label ranker can abstain to a certain degree. To realize label ranking with a reject option, we propose a method based on ensemble learning techniques. First empirical results are presented showing great promise for the usefulness of the approach. <img src="http://cdn.slidesharecdn.com/cheng-ecml09plslides-091105172827-phpapp01-thumbnail-2?1257464440" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> In label ranking, the problem is to learn a mapping from instances to rankings over a finite set of predefined class labels. In this paper, we consider a generalization of this problem, namely label ranking with a reject option. Just like in conventional classification, where a classifier can refuse a presumably unreliable prediction, the idea is to concede a label ranker the possibility to abstain. More specifically, the label ranker is allowed to make a weaker prediction in the form of a partial instead of a total order. Thus, unlike a conventional classifier which either makes a prediction or not, a label ranker can abstain to a certain degree. To realize label ranking with a reject option, we propose a method based on ensemble learning techniques. First empirical results are presented showing great promise for the usefulness of the approach. Label Ranking with Partial Abstention using Ensemble Learning
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]]> 22 0 http://cdn.slidesharecdn.com/cheng-ecml09plslides-091105172827-phpapp01-thumbnail-2?1257464440 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Learning similarity functions from qualitative feedback http://www.slideshare.net/roywwcheng/learning-similarity-functions-from-qualitative-feedback
The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information. See more at www.chengweiwei.com]]>
The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information. See more at www.chengweiwei.com]]> Thu, 05 Nov 2009 23:25:53 GMT http://www.slideshare.net/roywwcheng/learning-similarity-functions-from-qualitative-feedback roywwcheng@slideshare.net(roywwcheng) Learning similarity functions from qualitative feedback roywwcheng The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information. See more at www.chengweiwei.com <img src="http://cdn.slidesharecdn.com/cheng-eccbr08slides-091105172613-phpapp01-thumbnail-2?1257464151" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information. See more at www.chengweiwei.com Learning similarity functions from qualitative feedback
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]]> 64 0 http://cdn.slidesharecdn.com/cheng-eccbr08slides-091105172613-phpapp01-thumbnail-2?1257464151 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted A Simple Instance-Based Approach to Multilabel Classi*cation Using the Mallows Model http://www.slideshare.net/roywwcheng/a-simple-instancebased-approach-to-multilabel-classication-using-the-mallows-model
Multilabel classi cation is an extension of conventional classifi cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classi cation, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. In this paper, we propose a new instance-based approach to multilabel classification, which is based on calibrated label ranking, a recently proposed framework that uni es multilabel classi cation and label ranking. Within this framework, instance-based prediction is realized is the form of MAP estimation, assuming a statistical distribution called the Mallows model.]]>
Multilabel classi cation is an extension of conventional classifi cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classi cation, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. In this paper, we propose a new instance-based approach to multilabel classification, which is based on calibrated label ranking, a recently proposed framework that uni es multilabel classi cation and label ranking. Within this framework, instance-based prediction is realized is the form of MAP estimation, assuming a statistical distribution called the Mallows model.]]> Thu, 05 Nov 2009 23:24:36 GMT http://www.slideshare.net/roywwcheng/a-simple-instancebased-approach-to-multilabel-classication-using-the-mallows-model roywwcheng@slideshare.net(roywwcheng) A Simple Instance-Based Approach to Multilabel Classi*cation Using the Mallows Model roywwcheng Multilabel classi*cation is an extension of conventional classifi*cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classi*cation, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. In this paper, we propose a new instance-based approach to multilabel classification, which is based on calibrated label ranking, a recently proposed framework that uni*es multilabel classi*cation and label ranking. Within this framework, instance-based prediction is realized is the form of MAP estimation, assuming a statistical distribution called the Mallows model. <img src="http://cdn.slidesharecdn.com/cheng-ecml09mldslides-091105172454-phpapp02-thumbnail-2?1257464248" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Multilabel classi*cation is an extension of conventional classifi*cation in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classi*cation, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. In this paper, we propose a new instance-based approach to multilabel classification, which is based on calibrated label ranking, a recently proposed framework that uni*es multilabel classi*cation and label ranking. Within this framework, instance-based prediction is realized is the form of MAP estimation, assuming a statistical distribution called the Mallows model. A Simple Instance-Based Approach to Multilabel Classi cation Using the Mallows Model
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]]> 31 0 http://cdn.slidesharecdn.com/cheng-ecml09mldslides-091105172454-phpapp02-thumbnail-2?1257464248 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Decision tree and instance-based learning for label ranking http://www.slideshare.net/roywwcheng/decision-tree-and-instancebased-learning-for-label-ranking-1692094
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See more at www.chengweiwei.com]]> Tue, 07 Jul 2009 15:26:06 GMT http://www.slideshare.net/roywwcheng/decision-tree-and-instancebased-learning-for-label-ranking-1692094 roywwcheng@slideshare.net(roywwcheng) Decision tree and instance-based learning for label ranking roywwcheng See more at www.chengweiwei.com <img src="http://cdn.slidesharecdn.com/cheng-icml09slides-090707102612-phpapp01-thumbnail-2?1257464110" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> See more at www.chengweiwei.com Decision tree and instance-based learning for label ranking
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]]> 332 0 http://cdn.slidesharecdn.com/cheng-icml09slides-090707102612-phpapp01-thumbnail-2?1257464110 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted A new instance-based label ranking approach using the Mallows model http://www.slideshare.net/roywwcheng/a-new-instancebased-label-ranking-approach-using-the-mallows-model
See more at www.chengweiwei.com]]>
See more at www.chengweiwei.com]]> Tue, 07 Jul 2009 15:25:06 GMT http://www.slideshare.net/roywwcheng/a-new-instancebased-label-ranking-approach-using-the-mallows-model roywwcheng@slideshare.net(roywwcheng) A new instance-based label ranking approach using the Mallows model roywwcheng See more at www.chengweiwei.com <img src="http://cdn.slidesharecdn.com/cheng-isnn09slides-090707102512-phpapp02-thumbnail-2?1257464063" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> See more at www.chengweiwei.com A new instance-based label ranking approach using the Mallows model
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]]> 184 0 http://cdn.slidesharecdn.com/cheng-isnn09slides-090707102512-phpapp02-thumbnail-2?1257464063 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Instance based label ranking using the Mallows models http://www.slideshare.net/roywwcheng/instance-based-label-ranking-using-the-mallows-models
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See more at www.chengweiwei.com]]> Tue, 07 Jul 2009 01:06:17 GMT http://www.slideshare.net/roywwcheng/instance-based-label-ranking-using-the-mallows-models roywwcheng@slideshare.net(roywwcheng) Instance based label ranking using the Mallows models roywwcheng See more at www.chengweiwei.com <img src="http://cdn.slidesharecdn.com/cheng-mlss08slides-090706200627-phpapp02-thumbnail-2?1257464174" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> See more at www.chengweiwei.com Instance based label ranking using the Mallows models
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]]> 122 0 http://cdn.slidesharecdn.com/cheng-mlss08slides-090706200627-phpapp02-thumbnail-2?1257464174 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Ranking the skyline: an application of preference learning http://www.slideshare.net/roywwcheng/ranking-the-skyline-an-application-of-preference-learning
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See more at www.chengweiwei.com]]> Tue, 07 Jul 2009 00:59:51 GMT http://www.slideshare.net/roywwcheng/ranking-the-skyline-an-application-of-preference-learning roywwcheng@slideshare.net(roywwcheng) Ranking the skyline: an application of preference learning roywwcheng See more at www.chengweiwei.com <img src="http://cdn.slidesharecdn.com/cheng-gci08slides-090706200008-phpapp01-thumbnail-2?1257464197" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> See more at www.chengweiwei.com Ranking the skyline: an application of preference learning
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]]> http://public.slidesharecdn.com/images/userImage_SMALL.gif http://cdn.slidesharecdn.com/cheng-ecml09slides-091105173054-phpapp01-thumbnail?1257464782 roywwcheng/combining-instancebased-learning-and-logistic-regression-for-multilabel-classication Combining Instance-Bas... http://cdn.slidesharecdn.com/cheng-ecml09plslides-091105172827-phpapp01-thumbnail?1257464440 roywwcheng/label-ranking-with-partial-abstention-using-ensemble-learning Label Ranking with Par... http://cdn.slidesharecdn.com/cheng-eccbr08slides-091105172613-phpapp01-thumbnail?1257464151 roywwcheng/learning-similarity-functions-from-qualitative-feedback Learning similarity fu...