Abstract
The Statistical Query model was introduced in [6] to handle noise in the well-known PAC model. In this model the learner gains information about the target concept by asking for various statistics about it. Characterizing the number of queries required by learning a given concept class under fixed distribution was already considered in [3] for weak learning; then in [8] strong learnability was also characterized. However, the proofs for these results in [3,10,8] (and for strong learnability even the characterization itself) are rather complex; our main goal is to present a simple approach that works for both problems. Additionally, we strengthen the result on strong learnability by showing that a class is learnable with polynomially many queries iff all consistent algorithms use polynomially many queries, and by showing that proper and improper learning are basically equivalent. As an example, we apply our results on conjunctions under the uniform distribution.
This work was supported in part by the Deutsche Forschungsgemeinschaft Grant SI 498/8-1, and the NKTH grant of the National Technology Programme 2008 (project codename AALAMSRK NTP OM-00192/2008) of the Hungarian government.
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Szörényi, B. (2009). Characterizing Statistical Query Learning: Simplified Notions and Proofs. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2009. Lecture Notes in Computer Science(), vol 5809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04414-4_18
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DOI: https://doi.org/10.1007/978-3-642-04414-4_18
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