* new training set, 4 iterations, 30 mins 020501 124.33 124.33 5000 124.33 020503 149.51 25.19 2000 74.76 020506 225.99 76.48 7000 75.33 020507 434.45 208.46 12000 108.61 020508 441.31 6.86 5000 88.26 020509 381.86 -59.45 8000 63.64 020510 477.95 96.09 5000 68.28 020513 474.63 -3.32 4000 59.33 020514 1004.96 530.33 13000 111.66 020516 1084.87 79.90 2000 108.49 020517 1066.88 -17.99 4000 96.99 020520 1067.23 0.36 2000 88.94 020521 1115.52 48.29 2000 85.81 * new training set, 4 iterations, 30 mins 020522 1099.01 -16.51 2000 78.50 020523 1173.45 74.44 3000 78.23 020524 1188.54 15.09 1000 74.28 020529 1655.36 466.82 3000 97.37 020530 1769.55 114.19 5000 98.31 020531 1784.53 14.98 2000 93.92 020603 2020.74 236.21 2000 101.04 020604 2405.12 384.37 5000 114.53 020606 2206.82 -198.30 4000 100.31 020610 2067.54 -139.28 2000 89.89 020611 2080.76 13.22 2000 86.70 020612 2528.77 448.00 8000 101.15 020613 2549.56 20.80 1000 98.06 020614 2626.55 76.99 6000 97.28 020617 2675.69 49.14 3000 95.56 020618 2670.61 -5.07 1000 92.09 020619 2715.52 44.90 4000 90.52 020620 2813.68 98.16 3000 90.76 * new training set, 4 iterations, 30 mins 020701 2891.01 77.34 7000 90.34 020702 3117.42 226.40 6000 94.47 020703 3424.67 307.25 11000 100.73 020705 3485.75 61.08 2000 99.59 020708 3781.00 295.25 11000 105.03 020709 3768.81 -12.19 1000 101.86 020711 3835.38 66.57 6000 100.93 020715 3878.15 42.77 5000 99.44 020716 3966.14 87.99 7000 99.15 020717 4155.93 189.79 5000 101.36 020718 4081.70 -74.23 3000 97.18 020719 4100.10 18.41 3000 95.35