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In many unsupervised learning applications both spatial and temporal regularities in the data need to be represented. Traditional clustering algorithms, which are commonly employed by unsupervised learning engines, lack the ability to naturally capture temporal dependencies. In supervised learning methods, temporal features are often learned through the use of a feedback (or recurrent) signal. Drawing...
In this paper we present a Dynamic Sampling Framework for use with multi-class imbalanced data containing any number of classes. The framework makes use of existing sampling techniques such as RUS, ROS, and SMOTE and ties the classification algorithm into the sampling process in a wrapper like manner. In doing so the framework is able to search for a desirably sampled training set, thus eliminating...
We address the problem of large-scale topic classification of web pages based on the minimal text available in the URLs. This problem is challenging because of the sparsity of feature vectors that are derived from the URL text, and the typical asymmetry between the cardinality of train and test sets due to non-availability of sufficient sets of annotated URLs for training and very large test sets...
This paper addresses the problem of multi-agent, multi-task assignment with multiple agent requirements on tasks for unmanned aerial vehicles by presenting the Consensus Based Grouping Algorithm. The algorithm is an extension of the Consensus Based Bundle Algorithm that converges to a conflict free, feasible solution of which previous algorithms are unable to account for. Furthermore the algorithm...
Wireless sensor networks (WSNs) are gaining more interest in variety of applications. Of their different characteristics and challenges, network lifetime and efficiency are the most considered issues in WSN-based systems. The scarcest WSN's resource is energy, and one of the most energy-expensive operations is route discovery and data transmission. This paper presents a novel design of a cooperative...
In this paper, two well-known algorithms: ARIMA and TDNN (Time Delay Neural Network) are applied to conduct the short term prediction of solar radiation. For the daily solar radiation series is non-stable due to the fast weather changing, monthly average solar radiation is adopted as the data source. As ARIMA model requires the time series to be stationary, first order difference is performed on the...
In cluster analysis, finding out the number of clusters, K, for a given dataset is an important yet very tricky task, simply because there is often no universally accepted correct or wrong answer for non-trivial real world problems and it also depends on the context and purpose of a cluster study. This paper presents a new hybrid method for estimating the predominant number of clusters automatically...
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