Name
Abstract | ||
|---|---|---|
In a recent work we have carried out CarpeDiem, a novel algorithm for the fast evaluation of Supervised Sequential Learning (SSL) classifiers. In this paper we point out some interesting unexpected aspects of the learning behavior of the HMPerceptron algorithm that affect CarpeDiemperformances. This observation is the starting point of an investigation about the internal working of the HMPerceptron, which unveils crucial details of the internal working of the HMPerceptron learning strategy. The understanding of these details, augment the comprehension of the algorithm meanwhile suggesting further enhancements. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74782-6_22 | AI*IA |
Keywords | Field | DocType |
empirical findings,theoretical tenets,hmperceptron algorithm,recent work,interesting unexpected aspect,crucial detail,novel algorithm,fast evaluation,supervised sequential learning,internal working | Stability (learning theory),Markov property,Computer science,Algorithm,Artificial intelligence,Generalization error,Sequence learning,Machine learning,Viterbi algorithm,Comprehension | Conference |
Volume | ISSN | Citations |
4733 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Esposito | 1 | 64 | 10.87 |
| Daniele P. Radicioni | 2 | 165 | 23.17 |