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Filters
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
[article]
2013
arXiv
pre-print
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In this paper, we present an automated method that orders the instances in a data set by complexity based on the their likelihood of being misclassified (instance hardness). ...
Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. ...
By filtering the noisy and outlier instances prior to training, AdaBoost does not overfit the noise since it is no longer in the training data. ...
arXiv:1312.4986v1
fatcat:6fjfvcj26nghnibrajk7knrx2i
Action retrieval with relevance feedback on YouTube videos
2011
Proceedings of the Third International Conference on Internet Multimedia Computing and Service - ICIMCS '11
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Among other techniques, we explore soft-assignment codebook clustering, feature pruning, motion and static features, Adaboost and ABRS-SVM for relevance feedback. ...
In this paper, we investigate applying content-based retrieval with relevance feedback to the popular YouTube human action dataset[8], using a variety of methods to extract and compare features, in order ...
Feature type Cluster assign
RF
1
Static
Hard
Naive
2
Motion
Hard
Naive
3
Hybrid
Hard
Naive
4
Hybrid
Soft
Naive
5
Hybrid
Hard
Adaboost
6
Hybrid
Hard
ABRS-SVM ...
doi:10.1145/2043674.2043687
dblp:conf/icimcs/JonesS11
fatcat:migvpjz545buzofsuvccnnuwmi
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
[article]
2016
arXiv
pre-print
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In this paper, we present a tree-based classifier: Attention Tree (ATree) for large-scale image classification that uses recursive Adaboost training to construct a visual attention hierarchy. ...
The proposed framework has been evaluated on both Caltech-256 and SUN datasets and achieves accuracy improvement over state-of-the-art tree-based methods at significantly lower computational cost. ...
Fig. 1 ( 1 right) shows a toy example of an ATree based
Fig. 1 . 1 Fig. 1. ...
arXiv:1608.00611v1
fatcat:6h46ni5berajxmslj4dixmymbm
Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering
2005
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
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Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. ...
The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. ...
Acknowledgment I thank Xiang (Sean) Zhou, Xiangrong Chen, Tao Zhao, Adrian Barbu, Piotr Dollar, and Xiaodong Fan for helpful comments and assistance in getting some of the training datasets. ...
doi:10.1109/iccv.2005.194
dblp:conf/iccv/Tu05a
fatcat:7qy4k7vuerexzbc5avoq4ohz7a
Data Mining, Machine Learning and Big Data Analytics
2017
International Transaction of Electrical and Computer Engineers System
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The feasibility and challenges of the applications of deep learning and traditional data mining and machine learning methods in Big Data analytics are also analyzed and presented. ...
This paper analyses deep learning and traditional data mining and machine learning methods; compares the advantages and disadvantage of the traditional methods; introduces enterprise needs, systems and ...
Taking clustering as an example, a natural way of clustering big data is to extend existing methods (such as k-means) so that they can cope with the huge workloads. ...
doi:10.12691/iteces-4-2-2
fatcat:bk3lvlmikjdqhfejqrrxjdq5eq
Pruned Random Subspace Method for One-Class Classifiers
[chapter]
2011
Lecture Notes in Computer Science
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In this paper we show that the performance by the RSM can be noisy, and that pruning inaccurate classifiers from the ensemble can be more effective than using all available classifiers. ...
We propose to apply pruning to RSM of one-class classifiers using a supervised AUC criterion or an unsupervised consistency criterion. ...
Surprisingly, also AdaBoost did not perform very well. AdaBoost is originally developed to boost two-class classifiers by reweighing training objects. ...
doi:10.1007/978-3-642-21557-5_12
fatcat:sh7nqkruibf6nega2dxlxn2jky
Data-driven exemplar model selection
2014
IEEE Winter Conference on Applications of Computer Vision
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The current approaches do this by using mixtures of models, where each mixture is designed to capture one (or a few) axis of variation. ...
Another issue is the requirement of a large set of training images to capture such variations. ...
Acknowledgement: This work was supported in part by NSF Grant IIS1065336 and a Siebel Scholarship. The authors wish to thank Francisco Vicente and Ricardo Cabral for helpful discussions. ...
doi:10.1109/wacv.2014.6836080
dblp:conf/wacv/MisraSH14
fatcat:io35rfhlh5bx3pky57rxkjbqhi
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
2009
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. ...
The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. ...
A different method for ensemble pruning is to replace the ensemble by a set of classifiers using clustering. ...
doi:10.1109/tpami.2008.78
pmid:19110491
fatcat:jzpohrqz5veq3ocolk7ti4grqy
Top 10 algorithms in data mining
2007
Knowledge and Information Systems
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This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive ...
All remaining (18) nominations were then organized in 10 topics: association analysis, classification, clustering, statistical learning, bagging and boosting, sequential patterns, integrated mining, rough ...
The initial tree is then pruned to avoid overfitting. ...
doi:10.1007/s10115-007-0114-2
fatcat:zire76whxjdtzjmncv2nlvztme
Ensemble approaches for regression
2012
ACM Computing Surveys
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The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. ...
We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions ...
VI Summary on Methods for Ensemble Pruning Using Partitioning-Based Approaches Name Stopping criterion Evaluation measure Clustering by Partitioning: given number of clusters Partitioning: weighted distance ...
doi:10.1145/2379776.2379786
fatcat:7mfwduuedrcmzlwjr6sic2vyei
BioTextRetriever
2011
International Journal of Knowledge Discovery in Bioinformatics
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Finally we use that classifier to automatically enlarge the set of relevant papers by searching the MEDLINE using the automatically constructed classifier. ...
In current web sites and data bases of sequences there are, usually, a set of curated paper references linked to each sequence. ...
We have applied a collection of base learners and also ensemble learners. ...
doi:10.4018/jkdb.2011070102
fatcat:2m6zqivc75au3li6blrjwhivym
Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer
2022
Mathematical Problems in Engineering
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The performance of the ensemble model in both platforms is compared based on metrics like accuracy, precision, recall, and sensitivity and investigated in detail. ...
Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases ...
Pruning reduces the size of decision trees by removing nodes of the tree that do not contribute to model training. ...
doi:10.1155/2022/9619102
fatcat:ynuu35h2yjeyvntt364y57jbvm
Generating Diverse Ensembles to Counter the Problem of Class Imbalance
[chapter]
2010
Lecture Notes in Computer Science
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One of the more challenging problems faced by the data mining community is that of imbalanced datasets. ...
We conclude by analyzing the performance of the ensembles, and showing that, overall, our technique provides a significant improvement. ...
Acknowledgements Work was supported in part by the NSF Grant ECCS-0926170 and the Notebaert Premier Fellowship. ...
doi:10.1007/978-3-642-13672-6_46
fatcat:vc5o5f6bend6pauz7yxjuoiwwy
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1912.05170v2 fatcat:tvsvhid26rdcphqdnlwf4lgh6uOther Versions
This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods. ...
Although deep neural networks are known to be relatively robust to label noise, their tendency to overfit data makes them vulnerable to memorizing even random noise. ...
Therefore, noise-robust alternatives of AdaBoost is proposed in literature, such as noise detection based AdaBoost [133] , rBoost [134] , RBoost1&RBoost2 [135] and robust multi-class AdaBoost [136 ...
arXiv:1912.05170v3
fatcat:k5zm5k5e5bevph2abvwxkwr7qm
Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases
2019
International Journal of Advanced Computer Science and Applications
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This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases ...
This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. ...
So avoid by overfitting, many decision tree algorithm used the pruning method. In this method, growing the decision is stopped while deleting the portions of the tree. ...
doi:10.14569/ijacsa.2019.0100940
fatcat:ekopo2ll6zgnjdqg4xpqqyz46q
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