## Learning Fixed-dimension Linear Thresholds From Fragmented Data (1999)

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Venue: | in Procs of the 1999 Conference on Computational Learning Theory |

Citations: | 2 - 2 self |

### BibTeX

@ARTICLE{Goldberg99learningfixed-dimension,

author = {Paul W. Goldberg},

title = {Learning Fixed-dimension Linear Thresholds From Fragmented Data},

journal = {in Procs of the 1999 Conference on Computational Learning Theory},

year = {1999},

volume = {171},

pages = {98--122}

}

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### Abstract

We investigate PAC-learning in a situation in which examples (consisting of an input vector and 0/1 label) have some of the components of the input vector concealed from the learner. This is a special case of Restricted Focus of Attention (RFA) learning. Our interest here is in 1-RFA learning, where only a single component of an input vector is given, for each example. We argue that 1-RFA learning merits special consideration within the wider eld of RFA learning. It is the most restrictive form of RFA learning (so that positive results apply in general), and it models a typical \data fusion" scenario, where we have sets of observations from a number of separate sensors, but these sensors are uncorrelated sources. Within this setting we study the well-known class of linear threshold functions, the characteristic functions of Euclidean half-spaces. The sample complexity (i.e. sample-size requirement as a function of the parameters) of this learning problem is aected by the input distri...