Instantaneously Trained Neural Networks

Instantaneously Trained Neural Networks Abhilash Ponnath Abstract: This paper presents a review of instantaneously trained neural networks (ITNNs). These networks trade learning time for size and, in the basic model, a new hidden node is created for each training sample. Various versions of the corner-classification family of ITNNs, which have f ound applications in artificial intelligence (AI), are described. Implementation issues are also considered. 1 Introduction The human brain, the most complex known living structure in the universe, has the nerve cell or neuron as its fundamental unit. The number of neurons and connections between the neurons is enormous; this ensemble enables the brain to surpass the computational capacity of supercomputers in existence today. Artificial neural networks (ANNs) are models of the brain, which implement the mapping, ƒ: X Y such that the task is completed in a "certain" sense.