machine-learning - Pegasos参数调整

我正在尝试调整pegasos算法参数而没有运气。

这是使用6个一维点的简单示例。

void dlib_svm_test()
{
    for(double lambda= 10e-10;lambda<=10e10;lambda*=10)
    {
        typedef dlib::matrix<double, 0, 1> sample_type;
        typedef dlib::linear_kernel<sample_type> kernel_type;

        dlib::svm_pegasos<kernel_type> pegasos;

        cout << lambda <<endl;

        pegasos.set_lambda(lambda);
        pegasos.set_kernel(kernel_type());

        std::vector<sample_type> samples(6);
        sample_type sample;
        sample.set_size(1);

        sample(0)= 188.0d;
        samples[0]= sample;
        sample(0)= 168.0d;
        samples[1]= sample;
        sample(0)= 191.0d;
        samples[2]= sample;
        sample(0)= 150.0d;
        samples[3]= sample;
        sample(0)= 154.0d;
        samples[4]= sample;
        sample(0)= 124.0d;
        samples[5]= sample;

        pegasos.train(samples[0],+1);
        pegasos.train(samples[1],+1);
        pegasos.train(samples[2],+1);
        pegasos.train(samples[3],-1);
        pegasos.train(samples[4],-1);
        pegasos.train(samples[5],-1);

        cout<< pegasos(samples[0]) <<endl;
        cout<< pegasos(samples[1]) <<endl;
        cout<< pegasos(samples[2]) <<endl;
        cout<< pegasos(samples[3]) <<endl;
        cout<< pegasos(samples[4]) <<endl;
        cout<< pegasos(samples[5]) <<endl;

        pegasos.clear();
    }
}

我得到的输出：

0.0000000010
-3963387.1199921928
-3541750.1923335334
-4026632.6591409920
-3162276.9574407390
-3246604.3429724714
-2614148.9514844813
0.0000000100
-1253333.0548153266
-1119999.7511116527
-1273333.0503708781
-999999.7777783460
-1026666.4385190808
-826666.4829635697
0.0000001000
-396338.7119995961
-354175.0192337657
-402663.2659144707
-316227.6957445183
-324660.4342976844
-261414.8951489388
0.0000010000
-125333.3054819095
-111999.9751115777
-127333.3050374593
-99999.9777782790
-102666.6438523454
-82666.6482968476
0.0000100000
-39633.8712003365
-35417.5019237890
-40266.3265918186
-31622.7695748963
-32466.0434302058
-26141.4895153846
0.0001000000
-12533.3305485679
-11199.9975115703
-12733.3305041176
-9999.9977782724
-10266.6643856720
-8266.6648301755
0.0010000000
-3963.3871204108
-3541.7501927916
-4026.6326595536
-3162.2769579343
-3246.6043434582
-2614.1489520294
0.0100000000
-1253.3330552344
-1119.9997515702
-1273.3330507840
-999.9997782725
-1026.6664390053
-826.6664835091
0.1000000000
-396.3387124203
-354.1750196940
-402.6632663292
-316.2276962404
-324.6604347856
-261.4148956963
1.0000000000
-125.3333059077
-111.9999755772
-127.3333054573
-99.9999782797
-102.6666443458
-82.6666488500
10.0000000000
-39.6338716427
-35.4175024067
-40.2663270281
-31.6227700943
-32.4660439415
-26.1414900875
100.0000000000
-12.5333310483
-11.1999980544
-12.7333309973
-9.9999983600
-10.2666649587
-8.2666654680
1000.0000000000
-3.7091542406
-3.3145634810
-3.7683428546
-2.9594317974
-3.0383499493
-2.4464638100
10000.0000000000
-0.4292670207
-0.3836003494
-0.4361170215
-0.3425003451
-0.3516336794
-0.2831336723
100000.0000000000
0.0372866667
0.0333200000
0.0378816667
0.0297500000
0.0305433333
0.0245933333
1000000.0000000000
0.0037286667
0.0033320000
0.0037881667
0.0029750000
0.0030543333
0.0024593333
10000000.0000000000
0.0003728667
0.0003332000
0.0003788167
0.0002975000
0.0003054333
0.0002459333
100000000.0000000000
0.0000372867
0.0000333200
0.0000378817
0.0000297500
0.0000305433
0.0000245933
1000000000.0000000000
0.0000037287
0.0000033320
0.0000037882
0.0000029750
0.0000030543
0.0000024593
10000000000.0000000000
0.0000003729
0.0000003332
0.0000003788
0.0000002975
0.0000003054
0.0000002459
100000000000.0000000000
0.0000000373
0.0000000333
0.0000000379
0.0000000297
0.0000000305
0.0000000246

因此，我将所有样本预测为阴性或阳性的问题。

更新：

问题解决了：

https://github.com/davisking/dlib/issues/49

最佳答案

这是一种基于随机梯度下降的在线学习算法。每次调用train（）都需要一个梯度步骤，因此您必须多次调用train（）6次。

使用批处理算法而不是在线算法可能也更好。使用本指南选择适合您任务的一个：http://dlib.net/ml_guide.svg

关于machine-learning - Pegasos参数调整，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/33178144/