Pattern Recognition and Machine Learning

14.3. Boosting 661 form E = e−αm/^2 ∑ n∈Tm w(nm)+eαm/^2 ∑ n∈Mm w(nm) =(eαm/^2 −e−αm/^2 ) ∑N n=1 wn(m)I(ym(xn) =tn)+e−αm/^2 ∑N n= ...

662 14. COMBINING MODELS Figure 14.3 Plot of the exponential (green) and rescaled cross-entropy (red) error functions along with ...

14.4. Tree-based Models 663 Figure 14.4 Comparison of the squared error (green) with the absolute error (red) showing how the la ...

664 14. COMBINING MODELS Figure 14.6 Binary tree corresponding to the par- titioning of input space shown in Fig- ure 14.5. x 1 ...

14.4. Tree-based Models 665 olds) to minimize the sum-of-squares error is usually computationally infeasible due to the combinat ...

666 14. COMBINING MODELS of performance. If we definepτkto be the proportion of data points in regionRτ assigned to classk, wher ...

14.5. Conditional Mixture Models 667 logistic regression models (Section 14.5.2). In the simplest case, the mixing coeffi- cient ...

668 14. COMBINING MODELS Figure 14.7 Probabilistic directed graph representing a mixture of linear regression models, defined by ...

14.5. Conditional Mixture Models 669 problem, in which the term corresponding to thenthdata point carries a weighting coefficien ...

670 14. COMBINING MODELS −1 −0.5 0 0.5 1 −1.5 −1 −0.5 0 0.5 1 1.5 −1 −0.5 0 0.5 1 −1.5 −1 −0.5 0 0.5 1 1.5 −1 −0.5 0 0.5 1 −1.5 ...

14.5. Conditional Mixture Models 671 Figure 14.9 The left plot shows the predictive conditional density corresponding to the con ...

672 14. COMBINING MODELS The M step involves maximization of this function with respect toθ, keepingθold, and henceγnk, fixed. M ...

14.5. Conditional Mixture Models 673 Figure 14.10 Illustration of a mixture of logistic regression models. The left plot shows d ...

674 14. COMBINING MODELS densities and the mixing coefficients share the hidden units of the neural network. Furthermore, in the ...

Exercises 675 14.6 ( ) www By differentiating the error function (14.23) with respect toαm, show that the parametersαm in the Ad ...

676 14. COMBINING MODELS 14.16 ( ) Extend the logistic regression mixture model of Section 14.5.2 to a mixture of softmax classi ...

Appendix A Data Sets In this appendix, we give a brief introduction to the data sets used to illustrate some of the algorithms d ...

678 A. DATA SETS Figure A.1 One hundred examples of the MNIST digits chosen at ran- dom from the training set. Oil Flow This is ...

A. DATA SETS 679 Figure A.2 The three geometrical configurations of the oil, water, and gas phases used to generate the oil- flo ...

680 A. DATA SETS tribution is to be reconstructed from an number of one-dimensional averages. Here there are far fewer line meas ...