Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
Sivu 40
TABLE 2.3 The capacities of some Ø machines Decision boundary in pattern space implemented by machine Capacity Hyperplane Hypersphere General quadric surface rth - order polynomial surface 2d + 1 ) 2d + 2 ) ( d + 1 ) ( d + 2 ) dt 2 z ...
TABLE 2.3 The capacities of some Ø machines Decision boundary in pattern space implemented by machine Capacity Hyperplane Hypersphere General quadric surface rth - order polynomial surface 2d + 1 ) 2d + 2 ) ( d + 1 ) ( d + 2 ) dt 2 z ...
Sivu 129
The quadric function g ( x ) can now be written as g ( x ) = | QX | 2 – Q2X2 + B'X + C ( A.9 ) Equation ( A - 9 ) suggests an implementation employing weights and summers . The term Q.X2 is computed by summing the squares of the outputs ...
The quadric function g ( x ) can now be written as g ( x ) = | QX | 2 – Q2X2 + B'X + C ( A.9 ) Equation ( A - 9 ) suggests an implementation employing weights and summers . The term Q.X2 is computed by summing the squares of the outputs ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |