| 31. | The transformation that gives the minimal cost function is chosen as the model for head motion.
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| 32. | The cost function can be much more complicated.
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| 33. | Having established the cost function, the algorithm simply uses gradient descent to find the optimal transformation.
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| 34. | These viable solutions are judged by their satisfaction of a series of measures or cost functions.
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| 35. | These are generally referred to as cost functions and the other measures are treated as constraints.
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| 36. | This algorithm calculates the shortest path using the number of optical routers as the cost function.
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| 37. | Concave cost functions represent this economy of scale.
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| 38. | The adaptation step of the neural gas can be interpreted as gradient descent on a cost function.
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| 39. | At the long end, a regression technique with a cost function that values smoothness might be used.
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| 40. | This algorithm calculates the p shortest paths using the number of optical routers as the cost function.
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