## The Mathematical Foundations of Learning MachinesNeural networks research is unified by contributions from computer science, electrical engineering, physics, statistics, cognitive science and neuroscience. Author Nilsson is recognized for his presentation of intuitive geometric and statistical theories. Annotation copyrighted by Book News, Inc., Portland, OR |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Common terms and phrases

achieved adjusted apply assume bank called changes Chapter classifier cluster committee components Computation consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation Development discriminant functions discussed distance distribution equal error-correction estimates example exists expression FIGURE fixed given implemented important initial known layered machine learning linear dichotomies linear machine linearly separable matrix measurements neural networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane plane points positive possible presented probability problem proof properties proved PWL machine quadric regions respect response rule selection separable sequence shown side solution space Stanford Statistical step subsidiary Suppose Systems theorem theory threshold training methods training procedure training sequence training subsets transformation units values weight vectors zero