K-means based on active learning for support vector machine

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Journal of Hydrology and Hydromechanics ◽

10.2478/v10098-012-0002-7 ◽

2012 ◽

Vol 60 (1) ◽

pp. 16-32 ◽

Cited By ~ 3

Author(s):

Hamid Shahraiyni ◽

Mohammad Ghafouri ◽

Saeed Shouraki ◽

Bahram Saghafian ◽

Mohsen Nasseri

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector ◽

Learning Method ◽

Runoff Simulation ◽

Karoon River ◽

Discharge Data ◽

Daily Streamflow ◽

Runoff Modeling ◽

Active Learning Method

Comparison Between Active Learning Method and Support Vector Machine for Runoff ModelingIn this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3s-1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m3s-1, 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling.

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