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Using subgoal chaining to address the local minimum problem

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Figure

Figure 1.1: Stylised view of a fully connected and strictly layered feedforward neural net­
Figure 2.1; Stylised view of error-weight space with weight axes Wa and Wb and error is indicated by the number on the contour lines
Figure 2.2: Stylised view of error-weight space with weight axes Wa and Wy and an axis for the error E
Figure 2.3: dashed error contour lines are not to scale but denote the increasing height of this simpleStylised view of weight space with 2  weight axes Wa and Wb- Numbers on the  quadratic error function around its one minimum which is therefore a global minimum.
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