Name
Abstract | ||
|---|---|---|
To solve online nonlinear problems, usually, a set of misclassified observed examples (defined as support set) should be stored in the internal memory for computing kernel values. With the increase of a large scale of training data, computing all the kernel values is expensive and also can lead to an out-of-memory problem. In the paper, a fusion strategy is proposed to compress the size of support set for online learning and the fused kernel can best represent the current instance and its nearest one in the support set in the previous time. The proposed algorithm is based on Perceptron-like method, and thus it is called as Fuseptron. Different from the most recently proposed nonlinear online algorithms, the internal memory can be bounded in Fuseptron and the mistake bound is also derived. Experiments carried out on one synthetic and four real large-scale datasets validate the effectiveness and efficiency of Fuseptron compared to the state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2351316.2351332 | BigMine |
Keywords | Field | DocType |
current instance,nonlinear online algorithm,online learning,online nonlinear problem,perceptron-like method,large-scale data,fused kernel,proposed algorithm,support set,online classification,internal memory,kernel value,kernel fused perceptron,online algorithm | Online machine learning,Data mining,Radial basis function kernel,Kernel perceptron,Kernel embedding of distributions,Computer science,Tree kernel,Polynomial kernel,Kernel method,Perceptron | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijun He | 1 | 0 | 1.35 |
| Mingmin Chi | 2 | 488 | 35.97 |
| Wenqiang Zhang | 3 | 5 | 6.50 |