We are developing a hand-printed character recognition system using a multi(cid:173) layered neural net trained through backpropagation. We report on results of training nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a sty(cid:173) lus digitizer. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing practical pattern recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy.

One of the most common applications of machine learning is pattern recognition. Computers that use well-trained algorithms recognize animals in photos, anomalies in stock fluctuations, and signs of cancer in mammograms much better than humans. Let us find out what lies behind this complex process. Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine's memory.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

Andrew Rubin sits with a Surface tablet, watching a white skeletal hand open and close on its screen. Rubin's right hand was amputated a year ago, but he follows these motions with a special device fitted to his upper arm. Electrodes on his arm connect to a box that records the patterns of nerve signals firing, allowing Rubin to train a prosthetic limb to act like a real hand. "When I think of closing a hand, it's going to contract certain muscles in my forearm," he says. "The software recognizes the patterns created when I flex or extend a hand that I do not have."

Despite the prevalence of the Computational Theory of Mind and the Connectionist Model, the establishing of the key principles of the Cognitive Science are still controversy and inconclusive. This paper proposes the concept of PATTERN RECOGNITION as NECESSARY AND SUFFICIENT PRINCIPLE for a general cognitive science modeling, in a very ambitious scientific proposal. A formal physical definition of the pattern recognition concept is also proposed to solve many key conceptual gaps on the field.