… Without any prior knowledge of the underlying physics, our algorithm discovers the
intrinsic dimension of the observed dynamics and identifies candidate sets of state variables. …

… We show that our feature vector representation works well for automated knowledge discovery
using rule learning. We then employ feature subset selection using a genetic algorithm to …

… automated discovery algorithms in econometrics leads me to believe that these methods
will play an important role in the future use of applied econometrics+ I also believe that they …

… in automated discovery is … more data in unit time; hence, automated discovery methods
fare better with simulation. In the future, we believe that new algorithms for automated discovery, …

… In this chapter, the research field of algorithms that automatically discover process
models from event logs is described. First, quality criteria for models are discussed, and how …

… Next, we apply a classification tree algorithm to the labeled rows, to learn a set of rules that
defines the partition; these rules are then converted to a selection predicate. A more detailed …

… This book proposes a new approach to the design of data mining algorithms. In… algorithm
design, this book proposes systematically automating the design of data mining algorithms with …

… This network appears in Figure 2-3; to test the lattice inference algorithms we will
construct a. ‘sandwich’ of intersections and unions about and Then when we create a ‘filling’ …

… We are asking the user to specify neither the methods nor the algorithms used for analysis.
User input is still mainly solicited regarding variable metadata properties and some general …

… We use a betweenness centrality algorithm that can rapidly find communities within a graph
representing information flows. We apply this algorithm to an email corpus of nearly one …