Matches in SemOpenAlex for { <https://semopenalex.org/work/W2626968741> ?p ?o ?g. }
Showing items 1 to 55 of
55
with 100 items per page.
- W2626968741 endingPage "134" @default.
- W2626968741 startingPage "125" @default.
- W2626968741 abstract "Artificial intelligence is present in many modern computer science applications. The question of effectively learning parameters of such models even with small data samples is still very active. It turns out that restricting conditional probabilities of a probabilistic model by monotonicity conditions might be useful in certain situations. Moreover, in some cases, the modeled reality requires these conditions to hold. In this article we focus on monotonicity conditions in Bayesian Network models. We present an algorithm for learning model parameters, which satisfy monotonicity conditions, based on gradient descent optimization. We test the proposed method on two data sets. One set is synthetic and the other is formed by real data collected for computerized adaptive testing. We compare obtained results with the isotonic regression EM method by Masegosa et al. which also learns BN model parameters satisfying monotonicity. A comparison is performed also with the standard unrestricted EM algorithm for BN learning. Obtained experimental results in our experiments clearly justify monotonicity restrictions. As a consequence of monotonicity requirements, resulting models better fit data." @default.
- W2626968741 created "2017-06-23" @default.
- W2626968741 creator A5005522432 @default.
- W2626968741 creator A5032346261 @default.
- W2626968741 date "2017-01-01" @default.
- W2626968741 modified "2023-10-07" @default.
- W2626968741 title "Monotonicity in Bayesian Networks for Computerized Adaptive Testing" @default.
- W2626968741 cites W1989486129 @default.
- W2626968741 cites W2178330977 @default.
- W2626968741 cites W4241084003 @default.
- W2626968741 cites W618991135 @default.
- W2626968741 doi "https://doi.org/10.1007/978-3-319-61581-3_12" @default.
- W2626968741 hasPublicationYear "2017" @default.
- W2626968741 type Work @default.
- W2626968741 sameAs 2626968741 @default.
- W2626968741 citedByCount "1" @default.
- W2626968741 countsByYear W26269687412022 @default.
- W2626968741 crossrefType "book-chapter" @default.
- W2626968741 hasAuthorship W2626968741A5005522432 @default.
- W2626968741 hasAuthorship W2626968741A5032346261 @default.
- W2626968741 hasConcept C107673813 @default.
- W2626968741 hasConcept C119857082 @default.
- W2626968741 hasConcept C134306372 @default.
- W2626968741 hasConcept C154945302 @default.
- W2626968741 hasConcept C33724603 @default.
- W2626968741 hasConcept C33923547 @default.
- W2626968741 hasConcept C41008148 @default.
- W2626968741 hasConcept C72169020 @default.
- W2626968741 hasConceptScore W2626968741C107673813 @default.
- W2626968741 hasConceptScore W2626968741C119857082 @default.
- W2626968741 hasConceptScore W2626968741C134306372 @default.
- W2626968741 hasConceptScore W2626968741C154945302 @default.
- W2626968741 hasConceptScore W2626968741C33724603 @default.
- W2626968741 hasConceptScore W2626968741C33923547 @default.
- W2626968741 hasConceptScore W2626968741C41008148 @default.
- W2626968741 hasConceptScore W2626968741C72169020 @default.
- W2626968741 hasLocation W26269687411 @default.
- W2626968741 hasOpenAccess W2626968741 @default.
- W2626968741 hasPrimaryLocation W26269687411 @default.
- W2626968741 hasRelatedWork W1599577651 @default.
- W2626968741 hasRelatedWork W1775745059 @default.
- W2626968741 hasRelatedWork W1956930971 @default.
- W2626968741 hasRelatedWork W2016517455 @default.
- W2626968741 hasRelatedWork W2037915485 @default.
- W2626968741 hasRelatedWork W2152579687 @default.
- W2626968741 hasRelatedWork W2511279186 @default.
- W2626968741 hasRelatedWork W2902946190 @default.
- W2626968741 hasRelatedWork W2963058055 @default.
- W2626968741 hasRelatedWork W3080655609 @default.
- W2626968741 isParatext "false" @default.
- W2626968741 isRetracted "false" @default.
- W2626968741 magId "2626968741" @default.
- W2626968741 workType "book-chapter" @default.