Titlebook: Smiths Urologie; Emil A. Tanagho (Professor and Chairman),Jack W. M Book 1992 Springer-Verlag Berlin Heidelberg 1992 Chemotherapie.Diagnos

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E. A. Tanaghor One-class Support Vector Machines. One-class Support Vector Machines reduce the computational cost of testing new data by providing sparse solutions. However, all these techniques have relatively high computational requirements for training. Moreover, identifying anomalies based solely on density

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E. A. Tanaghoy to accurately determine this parameter. Popular methods are simple yet theoretically unfounded, such as searching for an elbow in the curve of a given cost measure. In contrast, statistically founded methods often make strict assumptions over the data distribution or come with their own optimizati

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E. A. Tanagho we learn to recognize future . instances over a continuous stream? We introduce ., which (.) estimates a . decision boundary between the rare and the majority class, (.) learns to recognize individual rare subclasses that exist within the training data, as well as (.) flags instances from previousl

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A. J. PalubinskasMLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using

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E. K. Lang we learn to recognize future . instances over a continuous stream? We introduce ., which (.) estimates a . decision boundary between the rare and the majority class, (.) learns to recognize individual rare subclasses that exist within the training data, as well as (.) flags instances from previousl

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B. A. Kogan,R. S. Hattnerd Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. .The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been