scholarly journals Constraint Learning: An Appetizer

2019 ◽  
pp. 232-249
Author(s):  
Stefano Teso
Keyword(s):  
Constraint Learning

WASP: A Native ASP Solver Based on Constraint Learning

2013 ◽  
pp. 54-66 ◽  
Author(s):  
Mario Alviano ◽  
Carmine Dodaro ◽  
Wolfgang Faber ◽  
Nicola Leone ◽  
Francesco Ricca
Keyword(s):  
Constraint Learning

Constraint learning based gradient boosting trees

2019 ◽  
Vol 128 ◽  
pp. 287-300 ◽  
Author(s):  
Abraham Israeli ◽  
Lior Rokach ◽  
Asaf Shabtai
Keyword(s):  
Gradient Boosting ◽  
Constraint Learning

Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

2019 ◽  
Vol 892 ◽  
pp. 219-227 ◽  
Author(s):  
Doreen Ying Ying Sim ◽  
Chee Siong Teh ◽  
Ahmad Izuanuddin Ismail
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Early Stopping ◽  
Classification Techniques ◽  
Obstructive Sleep ◽  
Component Variable ◽  
Research Outcome ◽  
Learning Research ◽  
Constraint Learning ◽  
Visualization Techniques

Based on the datasets from UCI and Obstructive Sleep Apnea, a disparate methodology of uncovering the visualization effects into the pushed support constraints of schema enumerated tree-based classification techniques is proposed and presented in this paper. This is to actively ‘wipe out’ the redundant growing effects of decision trees through itemset generation when visualization techniques are applied using Principal Component Analysis (PCA) and/or Principal Component Variable Grouping (PCVG) algorithms. Enumeration specification is based on the schema enumerated tree (SET) drawn after sorting out the features and characteristics on each dataset applied. The linchpin is to streamline the pre-tree classification effects for post-tree classification by using visualization techniques, i.e. PCA and/or PCVG, which are applied during the SET development. The over-fitting effects done during the SET development by the pushed support constraints can be counter-corrected by fewer PCA and/or PCVG imposed during visualization processes. The under-fitting effects done by the imprecise ‘early stopping’ of the SET development can be counter-corrected by greater PCA and/or PCVG imposed during the post-tree classification techniques through pushed SET support constraint learning. Research outcome on all the investigated datasets showed that the prediction accuracies have been profoundly improved after applying visualization of PCA and/or PCVG algorithms into the pushed SET-based or SET-based support constraints.

scholarly journals A SLAM Algorithm Based on Multi-Constraint Learning Model

2019 ◽  
Vol 1267 ◽  
pp. 012098
Author(s):  
Haibin Shi ◽  
Menghao Guo ◽  
Yuanbin Zou ◽  
Zhi Xu
Keyword(s):  
Learning Model ◽  
Slam Algorithm ◽  
Constraint Learning

Automatic image segmentation using constraint learning and propagation

2014 ◽  
Vol 24 ◽  
pp. 106-116 ◽  
Author(s):  
Cheolkon Jung ◽  
Juan Liu ◽  
Tian Sun ◽  
Licheng Jiao ◽  
Yanbo Shen
Keyword(s):  
Image Segmentation ◽  
Constraint Learning

scholarly journals Human–Machine Collaboration for Democratizing Data Science

2021 ◽  
pp. 379-402
Author(s):  
CléMent Gautrais ◽  
Yann Dauxais ◽  
Stefano Teso ◽  
Samuel Kolb ◽  
Gust Verbruggen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Predictive Modeling ◽  
Data Science ◽  
Data Selection ◽  
Artificial Intelligence Techniques ◽  
Constraint Learning ◽  
Spreadsheet Software

Everybody wants to analyse their data, but only few posses the data science expertise to do this. Motivated by this observation, we introduce a novel framework and system VisualSynth for human-machine collaboration in data science. Its aim is to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. VisualSynth relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.

scholarly journals On Irrelevant Literals in Pseudo-Boolean Constraint Learning

2020 ◽  
Author(s):  
Daniel Le Berre ◽  
Pierre Marquis ◽  
Stefan Mengel ◽  
Romain Wallon
Keyword(s):  
Cutting Planes ◽  
Np Hard ◽  
Clause Learning ◽  
Truth Value ◽  
Constraint Learning

Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting planes based inference is not as well understood as clause learning in conflict-driven clause learning solvers. In this paper, we show that PB constraints derived using cutting planes may contain irrelevant literals, i.e., literals whose assigned values (whatever they are) never change the truth value of the constraint. Such literals may lead to infer constraints that are weaker than they should be, impacting the size of the proof built by the solver, and thus also affecting its performance. This suggests that current implementations of PB solvers based on cutting planes should be reconsidered to prevent the generation of irrelevant literals. Indeed, detecting and removing irrelevant literals is too expensive in practice to be considered as an option (the associated problem is NP-hard).

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