Exploratory research contributes to the continued vitality of every discipline. The aim of exploratory research is to identify new tasks-tasks that cannot be solved by existing methods. Once a new task has been found, exploratory research seeks to develop a precise definition of the task and to understand the factors...
Most plant toxicology tests developed in support of environmental laws use a single stress applied to an individual plant. While tests using individual species or stresses require fewer resources and are easier to interpret, they are under increasing criticism for being unrealistic and missing important ecological interactions. The objective of...
The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any...
This paper applies learning techniques to make engineering optimization more efficient and reliable. When the function to be optimized is highly non-linear, the search space generally forms several disjoint convex regions . Unless gradient-descent search is begun in the right region, the solution found will be suboptimal. This paper formalizes...
Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes") . The definition is acquired by studying large collections of training examples of the form (xi, f(xi)) . Existing approaches to this problem include...
Many important application problems can be formalized as constrained non-linear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. Furthermore, they do not provide much insight into the structure of the problem space. This paper describes a method for discovering efficient rules that...
This paper describes efficient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This bias is useful for learning domains where many irrelevant features are present in the training data.
We first introduce FOCUS-2, a new algorithm that...