    AUTHOR = {Richard Kirkby},   TITLE = {Improving Hoeffding
Trees},   SCHOOL = {Department of   Computer Science, University
of Waikato},   YEAR = 2007,   HTTP =
{http://hdl.handle.net/10289/2568},   ABSTRACT = { Modern
information technology allows information to be   collected at a
far greater rate than ever before. So fast, in fact,   that the
main problem is making sense of it all. Machine learning
offers promise of a solution, but the field mainly focusses on
achieving high accuracy when data supply is limited. While this
has   created sophisticated classification algorithms, many do
not cope   with increasing data set sizes. When the data set
sizes get to a   point where they could be considered to
represent a continuous   supply, or data stream, then
incremental classification algorithms   are required. In this
setting, the effectiveness of an algorithm   cannot simply be
assessed by accuracy alone. Consideration needs to   be given to
the memory available to the algorithm and the speed at   whic