Question

我在Accord.net中使用SVM进行时间序列建模。我用可用数据训练一次（比如5000）。之后，我每秒都会获得新数据，我希望每秒使用一个数据以增量方式更新我的SVM机器，这可能吗？

Answer 1

虽然现在可以在Accord.NET中逐步学习SVM，但遗憾的是，此功能仅适用于线性机器。由于您正在使用时间序列，我假设您正在使用动态时间扭曲内核使用内核SVM。

如果您能够从序列中提取固定长度的要素，那么如果您将这些要素反馈到线性机器并使用随机梯度下降或Averaged Stochastic Gradient Descent进行训练，则可能会提出要求，如图所示下面：

// 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

accord.net svm增量训练

1 个答案: