### Abstract

Kernels are typically applied to linear algorithms whose weight vector is a linear combination of the feature vectors of the examples. On-line versions of these algorithms are sometimes called "additive updates" because they add a multiple of the last feature vector to the current weight vector. In this paper we have found a way to use special convolution kernels to efficiently implement "multiplicative" updates. The kernels are defined by a directed graph. Each edge contributes an input. The inputs along a path form a product feature and all such products build the feature vector associated with the inputs. We also have a set of probabilities on the edges so that the outflow from each vertex is one. We then discuss multiplicative updates on these graphs where the prediction is essentially a kernel computation and the update contributes a factor to each edge. After adding the factors to the edges, the total outflow out of each vertex is not one any more. However some clever algorithms re-normalize the weights on the paths so that the total outflow out of each vertex is one again. Finally, we show that if the digraph is built from a regular expressions, then this can be used for speeding up the kernel and re-normalization computations. We reformulate a large number of multiplicative update algorithms using path kernels and characterize the applicability of our method. The examples include efficient algorithms for learning disjunctions and a recent algorithm that predicts as well as the best pruning of a series parallel digraphs.

Original language | English |
---|---|

Pages (from-to) | 773-818 |

Number of pages | 46 |

Journal | Journal of Machine Learning Research |

Volume | 4 |

Issue number | 5 |

DOIs | |

Publication status | Published - Jul 1 2004 |

Externally published | Yes |

### All Science Journal Classification (ASJC) codes

- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence

## Fingerprint Dive into the research topics of 'Path kernels and multiplicative updates'. Together they form a unique fingerprint.

## Cite this

*Journal of Machine Learning Research*,

*4*(5), 773-818. https://doi.org/10.1162/1532443041424328