Results 31 to 40 of about 3,865,632 (281)

Unsupervised Feature Selection with Adaptive Structure Learning [PDF]

, 2015
The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using
Alelyani S., He X., Hou C., Krzanowski W., Li Z., Liu J., Liu X., Nie F., Nie F., Qian M., Takeuchi I., Yang Y., Zhao Z.   +12 more
core   +1 more source

Social structure learning in human anterior insula

eLife, 2020
Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and ...
Tatiana Lau, Samuel J Gershman, Mina Cikara   +2 more
doaj   +1 more source

Bayesian Network Structure Learning Approach Based on Searching Local Structure of Strongly Connected Components

IEEE Access, 2022
Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search & score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is ...
Kunhua Zhong, Yuwen Chen, Ju Zhang, Xiaolin Qin   +3 more
doaj   +1 more source

Learning to Predict the Cosmological Structure Formation [PDF]

, 2019
Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web.
Chen, Wei, Feng, Yu, He, Siyu, Ho, Shirley, Li, Yin, Póczos, Barnabás, Ravanbakhsh, Siamak   +6 more
core   +2 more sources

Bayesian Network Analysis for the Factors Affecting the 305-day Milk Productivity of Holstein Friesians

Journal of Agricultural Sciences, 2020
The variables affecting the milk productivity have been discussed in various articles through different methods. A recent study using path analysis shows that three variables significantly affect the 305-day milk yield of Holstein Friesian cows ...
Volkan Sevinç, Özge Akkuş, Çiğdem Takma, Öznur İşçi Güneri   +3 more
doaj   +1 more source

The Structure Transfer Machine Theory and Applications [PDF]

, 2019
Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to ...
Han, Jungong, Wang, Ze, Yang, Wankou, Zhang, Baochang, Zhen, Xiantong, Zhuo, Lian   +5 more
core   +2 more sources

An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors, 2021
Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions.
Bo Jin, Chunling Fu, Yong Jin, Wei Yang, Shengbin Li, Guangyao Zhang, Zheng Wang   +6 more
doaj   +1 more source

Supervised structure learning

Biological Psychology
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move - in the ensuing schemes - is to place priors on the selection of models, based upon expected free energy.
Karl J. Friston, Lancelot Da Costa, Alexander Tschantz, Alex Kiefer, Tommaso Salvatori, Victorita Neacsu, Magnus Koudahl, Conor Heins, Noor Sajid, Dimitrije Markovic, Thomas Parr, Tim Verbelen, Christopher L. Buckley   +12 more
openaire   +3 more sources

Exact Learning Augmented Naive Bayes Classifier

Entropy, 2021
Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal ...
Shouta Sugahara, Maomi Ueno
doaj   +1 more source

Person Re-identification with Correspondence Structure Learning [PDF]

, 2015
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-
Lin, Weiyao, Shen, Yang, Wang, Jingdong, Wu, Jianxin, Xu, Mingliang, Yan, Junchi   +5 more
core   +1 more source