Matches in SemOpenAlex for { <https://semopenalex.org/work/W2363911457> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W2363911457 abstract "Classification has been considered as a hot research area in machine learning, pattern recognition and data mining. Incremental learning is an effective method for learning the classification knowledge from massive data, especially in the situation of high cost in getting labeled training examples. Firstly, this paper discusses the difference between Bayesian estimation and classical parameter estimation and denotes the fundamental principle for incorporating the prior knowledge in Bayesian learning. Then we provide the incremental Bayesian learning model. This model explains the Bayesian learning process that changes the belief with the prior knowledge and new examples information. By selecting the Dirichlet prior distribution, we show this process in detail. In the second session, we mainly discuss the incremental process. For new examples for incremental learning, there exist two statuses: with labels and without labels. As for examples with labels, it is easy to update the classification parameter with the help of conjunct Dirichlet distribution. So it is the key point to learn from unlabeled examples. Different from the method provided by Kamal Nigam, which learns from unlabeled examples using EM algorithm, we focus on the next example that would be selected in learning. This paper gives a method measuring the classification loss with 0 1 loss. We will select the examples that minimize the classification loss. Meanwhile, to improve the algorithm performance, the pool based technique is introduced. For each turn, we only compute the classification loss for examples in pool. Because the basic operations in learning are updating the classification parameters and classifying test instances incrementally, we give their approximate expressions. For testing algorithm's efficiency, this paper makes an experiment on mushroom data set in UCI repository. The initial training set contains 6 labeled examples. Then several unlabeled examples are added. The final experimental results show that this algorithm is feasible and effective." @default.
- W2363911457 created "2016-06-24" @default.
- W2363911457 creator A5044229774 @default.
- W2363911457 date "2002-01-01" @default.
- W2363911457 modified "2023-09-25" @default.
- W2363911457 title "An Incremental Bayes Classification Model" @default.
- W2363911457 hasPublicationYear "2002" @default.
- W2363911457 type Work @default.
- W2363911457 sameAs 2363911457 @default.
- W2363911457 citedByCount "11" @default.
- W2363911457 countsByYear W23639114572012 @default.
- W2363911457 countsByYear W23639114572014 @default.
- W2363911457 countsByYear W23639114572018 @default.
- W2363911457 countsByYear W23639114572020 @default.
- W2363911457 countsByYear W23639114572021 @default.
- W2363911457 crossrefType "journal-article" @default.
- W2363911457 hasAuthorship W2363911457A5044229774 @default.
- W2363911457 hasConcept C107673813 @default.
- W2363911457 hasConcept C111919701 @default.
- W2363911457 hasConcept C119857082 @default.
- W2363911457 hasConcept C12267149 @default.
- W2363911457 hasConcept C134306372 @default.
- W2363911457 hasConcept C154945302 @default.
- W2363911457 hasConcept C169214877 @default.
- W2363911457 hasConcept C177769412 @default.
- W2363911457 hasConcept C182310444 @default.
- W2363911457 hasConcept C2524010 @default.
- W2363911457 hasConcept C26517878 @default.
- W2363911457 hasConcept C2781280628 @default.
- W2363911457 hasConcept C28719098 @default.
- W2363911457 hasConcept C33923547 @default.
- W2363911457 hasConcept C38652104 @default.
- W2363911457 hasConcept C41008148 @default.
- W2363911457 hasConcept C52001869 @default.
- W2363911457 hasConcept C58973888 @default.
- W2363911457 hasConcept C98045186 @default.
- W2363911457 hasConceptScore W2363911457C107673813 @default.
- W2363911457 hasConceptScore W2363911457C111919701 @default.
- W2363911457 hasConceptScore W2363911457C119857082 @default.
- W2363911457 hasConceptScore W2363911457C12267149 @default.
- W2363911457 hasConceptScore W2363911457C134306372 @default.
- W2363911457 hasConceptScore W2363911457C154945302 @default.
- W2363911457 hasConceptScore W2363911457C169214877 @default.
- W2363911457 hasConceptScore W2363911457C177769412 @default.
- W2363911457 hasConceptScore W2363911457C182310444 @default.
- W2363911457 hasConceptScore W2363911457C2524010 @default.
- W2363911457 hasConceptScore W2363911457C26517878 @default.
- W2363911457 hasConceptScore W2363911457C2781280628 @default.
- W2363911457 hasConceptScore W2363911457C28719098 @default.
- W2363911457 hasConceptScore W2363911457C33923547 @default.
- W2363911457 hasConceptScore W2363911457C38652104 @default.
- W2363911457 hasConceptScore W2363911457C41008148 @default.
- W2363911457 hasConceptScore W2363911457C52001869 @default.
- W2363911457 hasConceptScore W2363911457C58973888 @default.
- W2363911457 hasConceptScore W2363911457C98045186 @default.
- W2363911457 hasLocation W23639114571 @default.
- W2363911457 hasOpenAccess W2363911457 @default.
- W2363911457 hasPrimaryLocation W23639114571 @default.
- W2363911457 hasRelatedWork W1595981077 @default.
- W2363911457 hasRelatedWork W1625504505 @default.
- W2363911457 hasRelatedWork W1893167092 @default.
- W2363911457 hasRelatedWork W1991207565 @default.
- W2363911457 hasRelatedWork W2019011401 @default.
- W2363911457 hasRelatedWork W2056336673 @default.
- W2363911457 hasRelatedWork W2062179223 @default.
- W2363911457 hasRelatedWork W2108794916 @default.
- W2363911457 hasRelatedWork W2145908112 @default.
- W2363911457 hasRelatedWork W2157981294 @default.
- W2363911457 hasRelatedWork W2167974362 @default.
- W2363911457 hasRelatedWork W2360651852 @default.
- W2363911457 hasRelatedWork W2364913443 @default.
- W2363911457 hasRelatedWork W2387277551 @default.
- W2363911457 hasRelatedWork W2594608700 @default.
- W2363911457 hasRelatedWork W2922131323 @default.
- W2363911457 hasRelatedWork W2968530758 @default.
- W2363911457 hasRelatedWork W3006654992 @default.
- W2363911457 hasRelatedWork W3128289777 @default.
- W2363911457 hasRelatedWork W990771678 @default.
- W2363911457 isParatext "false" @default.
- W2363911457 isRetracted "false" @default.
- W2363911457 magId "2363911457" @default.
- W2363911457 workType "article" @default.