Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн
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6 Chapter 7Figure 7.1 Top Panel. Sliding‐window association (clique) of observations an...Figure 7.2 The transition schematic for the HMM with duration (HMMD).Figure 7.3 Viterbi column‐pointer match de‐segmentation rule. Table 1 and Ta...
7 Chapter 8Figure 8.1 Map.Figure 8.2 Chart.Figure 8.3 Overlapping charts.Figure 8.4 Curve. Note: The parameterization λ defines different curves...Figure 8.5 Function.Figure 8.6 Tangent Bundle, where “cross‐section” of TM (gives intuitive noti...Figure 8.7 1 Forms and cotangent bundle. There is a duality between 1 forms ...Figure 8.8 Gradient 1 form.Figure 8.9 Parallel transport.
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10 Chapter 11Figure 11.1 Gradient Ascent, Newton’s Ascent, Newton’s Ascent with restart....Figure 11.2 Metaheuristic #1: Euler’s Method – first‐order gradient ascent....Figure 11.3 Metaheuristic #2: Newton’s Method – second‐order gradient ascent...Figure 11.4 Metaheuristic #3: Newton’s Method – second‐order gradient ascent...Figure 11.5 Metaheuristic #4: (blind) hill climbing.Figure 11.6 Metaheuristic #5: steepest ascent hill climbing.Figure 11.7 Metaheuristic #6: steepest ascent hill climbing with restart.Figure 11.8 Metaheuristic #7: simulated annealing hill climbing.Figure 11.9 Metaheuristic #8: simulated annealing random restart.Figure 11.10 Metaheuristic #9: taboo search.Figure 11.11 Metaheuristic #13: evolutionary optimization (darwinian evoluti...