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Found 8 papers, 0 papers with code
Non-asymptotic approximations for Pearson's chi-square statistic and its application to confidence intervals for strictly convex functions of the probability weights of discrete distributions
In this paper, we develop a non-asymptotic local normal approximation for multinomial probabilities.
Sharp Frequency Bounds for Sample-Based Queries
A data sketch algorithm scans a big data set, collecting a small amount of data -- the sketch, which can be used to statistically infer properties of the big data set.
Selecting a number of voters for a voting ensemble
For a voting ensemble that selects an odd-sized subset of the ensemble classifiers at random for each example, applies them to the example, and returns the majority vote, we show that any number of voters may minimize the error rate over an out-of-sample distribution.
Ensemble Validation: Selectivity has a Price, but Variety is Free
Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example.
Some Theory For Practical Classifier Validation
WAG withholds a validation set, trains a holdout classifier on the remaining data, uses the validation data to validate that classifier, then adds the rate of disagreement between the holdout classifier and one trained using all in-sample data, which is an upper bound on the difference in error rates.
Improved Error Bounds Based on Worst Likely Assignments
Error bounds based on worst likely assignments use permutation tests to validate classifiers.
Validation of Matching
We introduce a technique to compute probably approximately correct (PAC) bounds on precision and recall for matching algorithms.
Speculate-Correct Error Bounds for k-Nearest Neighbor Classifiers
We introduce the speculate-correct method to derive error bounds for local classifiers.
