Learning from Streams Sanjay Jain?1, Frank Stephan??2 and Nan Ye3 1 Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore.
[email protected] 2 Department of Computer Science and Department of Mathematics, National University of Singapore, Singapore 117417, Republic of Singapore.
[email protected] 3 Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore.
[email protected] Abstract. Learning from streams is a process in which a group of learn- ers separately obtain information about the target to be learned, but they can communicate with each other in order to learn the target. We are interested in machine models for learning from streams and study its learning power (as measured by the collection of learnable classes). We study how the power of learning from streams depends on the two parameters m and n, where n is the number of learners which track a single stream of input each and m is the number of learners (among the n learners) which have to find, in the limit, the right description of the target. We study for which combinations m, n and m0, n0 the following inclusion holds: Every class learnable from streams with parameters m, n is also learnable from streams with parameters m0, n0. For the learning of uniformly recursive classes, we get a full characterization which depends m only on the ratio n ; but for general classes the picture is more compli- cated. Most of the noninclusions in team learning carry over to nonin- clusions with the same parameters in the case of learning from streams; but only few inclusions are preserved and some additional noninclusions hold.