id: 05112524
dt: j
an: 05112524
au: Wan, S.J.; Wong, S.K.M.
ti: A Partially Supervised Learning Algorithm for Linearly Separable Systems.
so: IEEE Transactions on Pattern Analysis and Machine Intelligence 14, No.10,
    1052-1056 (1992).
py: 1992
pu: Institute of Electrical and Electronics Engineers (IEEE), Washington, DC
la: EN
cc: 
ut: partially supervised learning; linearly separable systems; computational
    complexity; lower bound; learning (artificial intelligence)
ci: 
li: doi:10.1109/34.159907
ab: Summary: An important aspect of human learning is the ability to select
    effective samples to learn and utilize the experience to infer the
    outcomes of new events. This type of learning is characterized as
    partially supervised learning. A learning algorithm of this type is
    suggested for linearly separable systems. The algorithm selects a
    subset S from a finite set X of linearly separable vectors to construct
    a linear classifier that can correctly classify all the vectors in X.
    The sample set S is chosen without any prior knowledge of how the
    vectors in X-S are classified. The computational complexity of the
    algorithm is analyzed, and the lower bound on the size of the sample
    set is established.
rv: