【正文】 基于已有的工作特點，文中同時給出了約束和非約束學(xué)習(xí)的可能機會，以及關(guān)于馬爾科夫毯的可能應(yīng)用。參考文獻(xiàn): [1] Pearl, J., Probabilistic reasoning in expert systems [M] 1988,San Matego: Morgan Kaufmann.[2] Koller, D. and M. Sahami. Toward optimal feature selection. in the 13th International Conference on Machine Learning (ICML) [C]. 1996. Bari, Italy: Morgan Kaufmann.[3] Chickering, ., D. Geiger, and D. Heckerman, Learning Bayesian Network is NPHard [R], 1994, Microsoft. p. 22.[4] Campos, ., Z. Zeng, and Q. Ji, Efficient structure learning of Bayesian networks using constraints. Journal of Machine Learning Research (JMLR) [J], 2011. 12(11): p. 663689.[5] 王雙成, 苑森淼, and 王輝, 基于貝葉斯網(wǎng)絡(luò)的馬爾科夫毯預(yù)測學(xué)習(xí) [J]. 模式識別與人工智能, 2004. 17(1).[6] Spirtes, P., C. Glymour, and R. Scheines, Causation, Prediction, and Search [M] 2001: A Bradford Book.[7] Aliferis, C., I. Tsamardinos, and A. Statnikov. HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection [C]. in Annual Symposium on American Medical Informatics Association (AMIA) 2003.[8] Singh, M. and . Provan. Efficient learning of selective Bayesian network classifiers [C]. in 13th International Conference on Machine Learning (ICML). 1996. Morgan Kaufmann.[9] Margaritis, D. and S. Thrun. Bayesian network induction via local neighborhoods [C]. in Conference on Neural Information Processing Systems (NIPS). 1999. MIT Press.[10] Fu, S. and . Desmarais. Fast Markov blanket discovery algorithm via local learning within single pass [C]. in 21st Conference of the Canadian Society for Computational Studies of Intelligence (Canadian AI). 2008. Springer.[11] Fu, S. and . Desmarais. Tradeoff analysis of different Markov blanket local learning approaches [C]. in 12th PacificAsia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD). 2008. Osaka, Japan: Springer.[12] Tsamardinos, I., et al. Algorithms for large scale Markov blanket discovery [C]. in 16th International FLAIRS Conference. 2003. AAAI.[13] Fu, S., . Desmarais, and W. Chen. Reliability analysis of Markov blanket learning algorithms (19962010) [C]. in International Conference on Data Mining (DMIN). 2010. Las Vegas.[14] Yaramakala, S. and D. Margaritis. Speculative Markov blanket discovery for optimal feature selection [C]. in 5th IEEE International Conference on Data Mining (ICDM). 2005. Houston, Texas, USA: IEEE CSP.[15] Pena, ., et al., Towards scalable and data efficient learning of Markov boundaries [J]. International Journal of Approximate Reasoning, 2007. 45(2): p. 211232.[16] Zhang, Y., et al., An improved IAMB algorithm for Markov blanket discovery [J]. Journal of Computers, 2010. 5(11): p. 17551761.[17] Tsamardinos, I., . Aliferis, and A. Statnikov. Time and sample efficient discovery of Markov blankets and direct causal relations [C]. in 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 2003. ACM.[18] Fu, S. and . Desmarais. Local learning algorithm for Markov blanket discovery [C]. in Australian Conference on Artificial Intelligence. 2007. Gold Coast, Australia.[19] Morais, . and A. Aussem. A novel scalable and data efficient feature subset selection algorithm [C]. in European Conference on Machine Learning and Knowledge Discovery in Databases(ECML/PKDD). 2008. Antwerp, Belgium: Springer.[20] Zeng, Y., et al. Dynamic orderingbased search algorithm for Markov blanket discovery [C]. in 15th PacificAsia Conference on Data Mining. 2011. Shenzhen, China: Springer.[21] 郭坤, et al., 邏輯回歸分析的馬爾科夫毯學(xué)習(xí)算法 J]. 智能系統(tǒng)學(xué)報, 2012. 7(2).[22] Acid, S., . Campos, and M. Fern225。ndez, Scorebased methods for learning Markov boundaries by searching in constrained spaces J]. Data Mining and Knowledge Discovery, 2013. 26(1).[23] Acid, S., . Campos, and . Castellano, Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs [J]. Machine Learning, 2005. 59: p. 213235.[24] Bui, . and . Jun, Learning Bayesian network structure using Markov blanket [J]. Pattern Recognition Letters, 2012. 33(16): p. 21342140.[25] 周東梅, et al., 一種基于因果強度的局部因果結(jié)構(gòu)主動學(xué)習(xí)方法 J]. 計算機科學(xué), 2012. 39(11).歡迎您的光臨，！希望您提出您寶貴的意見，你的意見是我進(jìn)步的動力。贈語； 如果我們做與不做都會有人笑，如果做不好與做得好還會有人笑，那么我們索性就做得更好，來給人笑吧！ 現(xiàn)在你不玩命的學(xué)，以后命玩你。我不知道年少輕狂，我只知道勝者為王。不要做金錢、權(quán)利的奴隸；應(yīng)學(xué)會做“金錢、權(quán)利”的主人。什么時候離光明最近？那就是你覺得黑暗太黑的時候。最值得欣賞的風(fēng)景，是自己奮斗的足跡。壓力不是有人比你努力，而是那些比你牛幾倍的人依然比你努力。