The set \hat Y is then taken to be the set of patterns that have not been ruled out by the tests performed. We consider sequential testing strategies in which decisions are made iteratively, based on past outcomes, about which test to perform next and when to stop testing. Specifically, this sequential hierarchical training generates the underlying model for. These tests are then characterized by scope (|A|), power (or type II error) and algorithmic cost. training and test set performance in terms of both accuracy and loss. Each test has null type I error and the candidate sets A\subsetY are arranged in a hierarchy of nested partitions. To this end, we consider a family of hypothesis tests for Y\in A versus the nonspecific alternatives Y\in A^c. The focus here is then on pattern filtering: Given a large set Y of possible patterns or explanations, narrow down the true one Y to a small (random) subset \hat Y\subsetY of ``detected'' patterns to be subjected to further, more intense, processing. Secondary end points were objective response rate (ORR), safety, and quality of life. Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (``background'') is statistically dominant, and it is imperative to restrict intensive computation to genuinely ambiguous regions. Progression-free survival (PFS), PFS2, and overall survival (OS) were sequentially analyzed as primary end points according to a hierarchical sequential testing method. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). We explore the theoretical foundations of a ``twenty questions'' approach to pattern recognition.
0 kommentar(er)
