Sebanyak 2 item atau buku ditemukan

Progress in Artificial Intelligence. Knowledge Extraction, Multi-agent Systems, Logic Programming, and Constraint Solving

10th Portuguese Conference on Artificial Intelligence, EPIA 2001, Porto, Portugal, December 17-20, 2001. Proceedings

This book constitutes the refereed proceedings of the 10th Portuguese Conference on Artificial Intelligence, EPTA 2001, held in Porto, Portugal, in December 2001. The 21 revised long papers and 18 revised short papers were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections on extraction of knowledge from databases, AI techniques for financial time series analysis, multi-agent systems, AI logics and logic programming, constraint satisfaction, and AI planning.

This paper proposes a stochastic, and complete, backtrack search algorithm for
Propositional Satisfiability (SAT). In recent years, randomization has become
pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the
ones based on local search, often re- sort to randomization. Complete algorithms
also resort to randomization. These include, state-of-the-art backtrack search SAT
algorithms that often randomize variable selection heuristics. Moreover, it is plain
that the ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Gu, Q., Li, Z., Han, J.: Joint feature selection and subspace learning. In: IJCAI
Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p.
1294 (2011) Han, L., Zhang, Y.: Learning multi-level task groups in multi-task
learning ...