Accord.NET SVM增量培训

Question

我正在使用Accord.net中的SVM进行时间序列建模。我训练一次可用数据(例如5000(。之后，我每秒获得新数据，我想每秒使用一个数据以增量方式更新我的SVM机器，这是可能的吗？

Answer 1

现在可以在conced.net上逐步学习SVM，但不幸的是，此功能仅适用于线性机器。由于您正在使用时间序列，因此我假设您使用动态时间扭曲内核与内核SVM合作。

如果您能够从序列中提取固定长度的功能，那么如果将这些功能馈送到线性机器并使用随机梯度下降或平均随机梯度下降训练，您可能会问的可能是可能的，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