Learning from an ensemble of Receptive Fields

Abstract-In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as Shape Encoding Receptive Fields (SERF), is able to perform fast and accurate data classification and regression of multi-dimensional data. A SERF is a histogram structure that encodes the shape of multidimensional data relative to its center, in a manner similar to the neural coding of sensory stimulus by the receptive fields. The bins of this histogram represent a local region in an ndimensional space. During the training phase, an ensemble of K SERF structures are initialized and data is summarized into the corresponding bins of each SERF structure. The collection of local data summaries makes each SERF a coarse nonlinear data predictor over the entire feature space. The output prediction of an unknown query is computed by the weighted aggregation of the hypotheses of the ensemble of K SERFs. In our series of experimen...

• Type: Journal
• Year: 2009
• Where: IEEEICCI
• Authors: Hanlin Goh, Joo Hwe Lim, Chai Quek
• Keywords: Artificial Intelligence, IEEEICCI 2009, Receptive Fields, Serf Structure, Serfs