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Search Results for Collection: Computing and Mathematical Sciences Papers
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(Springer, 2012)We demonstrate the Advanced Data mining And Machine learning System (ADAMS), a novel workflow engine designed for rapid prototyping and maintenance of complex knowledge workflows. ADAMS does not require the user to manually ...
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(2010)Polycyclic Aromatic Hydrocarbons (PAHs) are compounds found in the environment that can be harmful to humans. They are typically formed due to incomplete combustion and as such remain after burning coal, oil, petrol, diesel, ... -
(2008)Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes ... -
(Springer, Berlin, 2006)The development of data-mining applications such as textclassification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the ...
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(2008)The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to ... -
(Springer, 2008)Nearest Neighbour Search (NNS) is one of the top ten data mining algorithms. It is simple and effective but has a time complexity that is the product of the number of instances and the number of dimensions. When the number ...
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(Springer, 2005)The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, ... -
(Springer Berlin, 2010)Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at ... -
(Springer, Berlin, 2008)Many applications require the ability to identify data that is anomalous with respect to a target group of observations, in the sense of belonging to a new, previously unseen ‘attacker’ class. One possible approach to this ...
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(2001)This paper describes my submission to one of the sub-problems formulated for the Predictive Toxicology Challenge 2001. The challenge is to predict the carcinogenicity of chemicals based on structural information only. I ...