MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY

These data are obtained by rotating the same 3D model used for the generation of the illumination examples: texture mapping techniques are then used to project a frontal view of

Figure 6: False positive matches: Images 1 and 2 show templates constructed from models Slow and Yield overlying the sign in image Slow2 (correlation values 0.56

The torque F(t) de- pends on the visual input; it was found (see Reichardt and Poggio, 1976) that it can be approximated, under situations of tracking and chasing, as a function

We now provide some simulations conducted on arbi- trary functions of the class of functions with bounded derivative (the class F ): Fig. 16 shows 4 arbitrary se- lected

In the rst stage the basis function parameters are de- termined using the input data alone, which corresponds to a density estimation problem using a mixture model in

The F-B algorithm relies on a factorization of the joint probability function to obtain locally recursive methods One of the key points in this paper is that the

Three criteria were used to measure the performance of the learning algorithm: experts' parameters deviation from the true values, square root of prediction MSE and

Although the applications in this paper were restricted to networks with Gaussian units, the Probability Matching algorithm can be applied to any reinforcement learning task