Agentic interpretability, using LLMs as proactive conversational teachers that model the user, is offered as a needed complement to black-box interpretability.
Sampling Method for Fast Training of Support Vector Data Description
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abstract
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .
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cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Because we have LLMs, we Can and Should Pursue Agentic Interpretability
Agentic interpretability, using LLMs as proactive conversational teachers that model the user, is offered as a needed complement to black-box interpretability.