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122,373
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
Abstract
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Cited by 2213 (20 self)
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the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real
Experiments with a New Boosting Algorithm
"... Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced ..."
Abstract
- Add to MetaCart
the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real
Experiments with a New Boosting Algorithm
"... Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced ..."
Abstract
- Add to MetaCart
the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real
Online Partitioning of Multi-Labeled Graphs∗
"... Graph partitioning is an old problem that is finding renewed in-terest in the era of big, complex datasets and parallel computing frameworks that can benefit from a proper partitiong of big graph data across multiple nodes in a cluster. In this paper we look into a specific instance of the problem t ..."
Abstract
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termed online graph partitioning that addresses the need to partition large graphs that do not fit in main memory. A neglected aspect of modern graph datasets is that real graphs have labels! Node labels may, for instance, correspond to categorical attributes (such as country, profession, participating
Active learning strategies for multi-label text classification
- In Proceedings of the 31st European Conference on Information Retrieval (ECIR 2009
, 2009
"... Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabeled examples in terms of how much further information they would carry, once manually labeled, for retraining a (hopeful ..."
Abstract
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Cited by 18 (4 self)
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(hopefully) better classifier. Research on active learning in text classification has so far concentrated on single-label classification; active learning for multi-label classification, instead, has either been tackled in a simulated (and, we contend, non-realistic) way, or neglected tout court
Local Rademacher Complexity for Multi-label Learning Local Rademacher Complexity for Multi-label Learning
"... We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the s ..."
Abstract
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of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound and a faster convergence rate. Compared to methods that minimize over all singular values, concentrating on the tail
26 Extended Abstract: Probabilistic Multi-Label Learning for Medical Data Probabilistic Multi-Label Learning for Medical Data
"... Abstract—We report on a probabilistic approach for the classi-fication of chronically ill patients. We rely on multi-label learning for its ability to represent in a natural way classification problems involving coexistence of diseases. We use a public clinical database for the evaluation of our pro ..."
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Abstract—We report on a probabilistic approach for the classi-fication of chronically ill patients. We rely on multi-label learning for its ability to represent in a natural way classification problems involving coexistence of diseases. We use a public clinical database for the evaluation of our
Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings
"... Abstract. In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result is obtaini ..."
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Abstract. In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result
Extracting Conflict-free Information from Multi-labeled Trees?
"... Abstract. A multi-labeled tree, or MUL-tree, is a phylogenetic tree where two or more leaves share a label, e.g., a species name. A MUL-tree can imply mul-tiple conflicting phylogenetic relationships for the same set of taxa, but can also contain conflict-free information that is of interest and yet ..."
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Abstract. A multi-labeled tree, or MUL-tree, is a phylogenetic tree where two or more leaves share a label, e.g., a species name. A MUL-tree can imply mul-tiple conflicting phylogenetic relationships for the same set of taxa, but can also contain conflict-free information that is of interest
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