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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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One-Class SVM Probabilistic Outputs.

Zhongyi Que, Chih-Jen Lin

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    Summary
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    This study introduces new methods for generating probabilistic outputs for one-class support vector machines (SVMs), a key technique in outlier detection. These novel approaches address the limitations of existing methods for unlabeled data, enhancing SVM

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • One-class support vector machines (SVM) are established for outlier detection with unlabeled data.
    • Standard one-class SVMs, like two-class SVMs, lack probabilistic outputs.
    • Existing probabilistic methods for two-class SVM are often unsuitable for one-class scenarios due to the absence of labels.

    Purpose of the Study:

    • To develop practical techniques for generating probabilistic outputs for one-class SVM.
    • To address the challenge of producing reliable probabilities from unlabeled data in outlier detection.
    • To enhance the interpretability and applicability of one-class SVM models.

    Main Methods:

    • Investigated limitations of existing two-class SVM probabilistic methods for one-class applications.
    • Proposed novel methods based on mimicking decision values of training data for probability generation.
    • Developed techniques specifically designed for the unique constraints of one-class classification.

    Main Results:

    • Demonstrated the effectiveness of the proposed probabilistic output methods.
    • Validated the techniques on both artificial and real-world datasets.
    • Showcased improved performance and utility of one-class SVM with probabilistic outputs.

    Conclusions:

    • The proposed methods provide a viable solution for obtaining probabilistic outputs from one-class SVM.
    • These techniques enhance the utility of one-class SVM in outlier detection and related applications.
    • Future work can build upon these methods to further refine probabilistic outlier detection.