The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances.
Using random spin glass instances as a benchmark, we find no evidence of quantum speedup when the entire data set is considered, and obtain inconclusive results when comparing subsets of instances on an instance-by-instance basis.
Similarly, while the actions of individual players can be plugged in to existing models easily on a per-instance basis, a more complicated problem is that of aggregating behavioral patterns of multiple players across multiple game instances, and potentially modifying model definitions that are reliant on operating assumptions about potential behaviors.
Second, current PPI research evaluates their performance on a "per-instance" basis.
In this work, we argued that the current "per-instance" basis performance evaluation method is not pragmatic in many realistic PPI extraction scenarios.
We expect that our method is more practical in real-world applications than conventional methods that are designed to balance the per-instance basis precision and recall.
In this regard, we propose a more pragmatic "per-relation" basis performance evaluation method instead of the conventional per-instance basis method.
Given these observations, we introduce a new performance evaluation method based on "per-relation" basis instead of the conventional per-instance basis, which is more pragmatic in practice.
The result suggests that our high-precision method can be also useful even in the conventional "per-instance" basis evaluation scenarios as we can achieve easy improvements of the performance of existing tools through the simple pipelining of our method.
According to the conventional per-instance basis evaluation, 100% accuracy is achieved only when a PPI tool correctly labels all the 8 positive instances as positive and the remainders as all negative.
"Decisions are made on a case by case basis for those instances," State Farm spokeswoman Heather Paul told HuffPost.
