PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples Article DOI:
Cite this article as: Long, P.M. & Tan, L. Machine Learning (1998) 30: 7. doi:10.1023/A:1007450326753 Abstract
We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q
with respect to product distributions from multiple-instance examples in the PAC model. Here, each example consists of d n elements of Q d together with a label indicating whether any of the n points is in the rectangle to be learned. We assume that there is an unknown product distribution D over Q such that all instances are independently drawn according to d D. The accuracy of a hypothesis is measured by the probability that it would incorrectly predict whether one of n more points drawn from D was in the rectangle to be learned. Our algorithm achieves accuracy ∈ with probability 1-δ in O (d 5 n 12/∈ 20 log 2 nd/∈δ time. PAC learning multiple-instance examples axis-aligned hyperrectangles Download to read the full article text References
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