Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
1,596
On the Stratication of Multi-Label Data
"... Abstract. Stratied sampling is a sampling method that takes into account the existence of disjoint groups within a population and pro-duces samples where the proportion of these groups is maintained. In single-label classication tasks, groups are dierentiated based on the value of the target variabl ..."
Abstract
- Add to MetaCart
variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear how stratied sam-pling could/should be performed. This paper investigates stratication in the multi-label data context. It considers two stratication methods for multi-label data and empirically
Mining multi-label data
- In Data Mining and Knowledge Discovery Handbook
, 2010
"... A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such d ..."
LEARNING FROM MULTI-LABEL DATA
, 2009
"... This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009. MLD’09 is devoted to multi-label learning, which is an emerging ..."
Abstract
-
Cited by 11 (2 self)
- Add to MetaCart
This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009. MLD’09 is devoted to multi-label learning, which is an emerging
Generating synthetic multi-label data streams
- In MLD ’09
, 2009
"... Abstract. There are many available methods for generating synthetic data streams. Such methods have been justified by the need to study the efficacy of algorithms on a theoretically infinite stream, and also a lack of real-world data of sufficient size. Although multi-label classification has attrac ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
Abstract. There are many available methods for generating synthetic data streams. Such methods have been justified by the need to study the efficacy of algorithms on a theoretically infinite stream, and also a lack of real-world data of sufficient size. Although multi-label classification has
A Study of Improving the Performance of Mining Multi-Valued and Multi-Labeled Data
, 2011
"... Abstract.Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-lab ..."
Abstract
- Add to MetaCart
Abstract.Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-labeled
Mining Multi-label Data Streams Using Ensemble-based Active Learning
"... Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data strea ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data
Learning a Distance Metric from Multi-instance Multi-label Data
"... Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image is represented by multiple key points (i.e. ..."
Abstract
-
Cited by 25 (7 self)
- Add to MetaCart
.e., instances) and is assigned to multiple object categories. In this paper, we study the problem of learning a distance metric from multi-instance multi-label data. It is significantly more challenging than the conventional setup of distance metric learning because it is difficult to associate instances in a
2 ND INTERNATIONAL WORKSHOP ON LEARNING FROM MULTI-LABEL DATA (MLD’10)
, 2010
"... This volume contains the research papers that form the programme of the 2nd International Workshop on Learning from Multi-Label Data (MLD’10), which was held in ..."
Abstract
- Add to MetaCart
This volume contains the research papers that form the programme of the 2nd International Workshop on Learning from Multi-Label Data (MLD’10), which was held in
An Ensemble-based Approach to Fast Classification of Multi-label Data Streams
"... Abstract—Network operators are continuously confronted with online events, such as online messages, blog updates, etc. Due to the huge volume of these events and the fast changes of the topics, it is critical to manage them promptly and effectively. There have been many softwares and algorithms deve ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
be able to consider the unique properties of multi-label stream data, such as large data volumes, label correlations and concept drifts. To address these challenges, in this paper, we propose an efficient and effective method for multi-label stream classification based on an ensemble of fading random
Min-Ling Zhang Learning from Multi-Label Data (多标记学习) Multi-Label Objects Sports Africa
"... instance label object Input space: represented by a single instance (feature vector) characterizing its properties Output space: associated with a single label characterizing its semantics Basic assumption real-world objects are unambiguous ..."
Abstract
- Add to MetaCart
instance label object Input space: represented by a single instance (feature vector) characterizing its properties Output space: associated with a single label characterizing its semantics Basic assumption real-world objects are unambiguous
Results 1 - 10
of
1,596
