accord.net支持向量机增量训练

Question

我正在使用Accord.net中的支持向量机进行时间序列建模。我用可用的数据(比如5000)训练它一次。在那之后，我每秒都会得到新的数据，我想以增量的方式更新我的SVM机器，每秒钟使用一个数据，这可能吗？

Answer 1

虽然现在可以在Accord.NET中增量地学习支持向量机，但不幸的是，此功能仅适用于线性机器。由于您使用的是时间序列，因此我假设您使用的是使用动态时间扭曲内核的内核SVM。

如果您能够从您的序列中提取固定长度的特征，那么如果您将这些特征提供给线性机器，并使用随机梯度下降或Averaged Stochastic Gradient Descent对其进行训练，则您所要求的可能是可能的，如下所示：

// In this example, we will learn a multi-class SVM using the one-vs-one (OvO)
// approach. The OvO approacbh can decompose decision problems involving multiple 
// classes into a series of binary ones, which can then be solved using SVMs.

// Ensure we have reproducible results
Accord.Math.Random.Generator.Seed = 0;

// We will try to learn a classifier
// for the Fisher Iris Flower dataset
var iris = new Iris();
double[][] inputs = iris.Instances; // get the flower characteristics
int[] outputs = iris.ClassLabels;   // get the expected flower classes

// We will use mini-batches of size 32 to learn a SVM using SGD
var batches = MiniBatches.Create(batchSize: 32, maxIterations: 1000,
   shuffle: ShuffleMethod.EveryEpoch, input: inputs, output: outputs);

// Now, we can create a multi-class teaching algorithm for the SVMs
var teacher = new MulticlassSupportVectorLearning<Linear, double[]>
{
    // We will use SGD to learn each of the binary problems in the multi-class problem
    Learner = (p) => new StochasticGradientDescent<Linear, double[], LogisticLoss>()
    {
        LearningRate = 1e-3, 
        MaxIterations = 1 // so the gradient is only updated once after each mini-batch
    }
};

// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Now, we can start training the model on mini-batches:
foreach (var batch in batches)
{
    teacher.Learn(batch.Inputs, batch.Outputs);
}

// Get the final model:
var svm = teacher.Model;

// Now, we should be able to use the model to predict 
// the classes of all flowers in Fisher's Iris dataset:
int[] prediction = svm.Decide(inputs);

// And from those predictions, we can compute the model accuracy:
var cm = new GeneralConfusionMatrix(expected: outputs, predicted: prediction);
double accuracy = cm.Accuracy; // should be approximately 0.973