Multi instance neural networks
Jan Ramon, Luc De Raedt · Lirias · 2000
This paper is concerned with extending neural networks to multi-instance learning. In multi-instance learning, each example corresponds to a set of tuples in a single relation. Furthermore, examples are classied as positive if at least one tuple (i.e. at least one attribute-value pair) satises certain conditions. If none of the tuples satisfy the requirements, the example is classied as negative. We will study how to extend standard neural networks (and backpropagation) to multi instance learning. It is clear that the multi-instance setting is more expressive than the attribute-value setting, but less expressive than e.g. relational learning or inductive logic programming. 2 The Multi-instance setting