Casasent D, Telfer B
Appl Opt. 1989 Jan 15;28(2):272-83. doi: 10.1364/AO.28.000272.
Most associative memory work has concentrated on autoassociative memories (AAMs). These associative processors provide reduced noise and error correction in their output data. We will consider heteroassociative memories (HAMs), which are needed to provide decisions on the class of the input data and inferences for subsequent processing. We derive new equations for the storage capacity and noise performance of HAMs, emphasize how they differ from those derived for AAMs, suggest new performance measures to be used, and show how different recollection vector encodings can improve HAM performance.
大多数关联记忆工作都集中在自联想记忆（AAM）上。这些关联处理器在其输出数据中提供了降低噪声和纠错功能。我们将考虑异联想记忆（HAM），它用于对输入数据的类别做出决策并为后续处理提供推理。我们推导了HAM的存储容量和噪声性能的新方程，强调了它们与AAM推导方程的不同之处，提出了要使用的新性能指标，并展示了不同的回忆向量编码如何提高HAM性能。
