We also give some lower estimates of the approximation error and discuss a specific greedy algorithm for approximation of convex domains in 2 .doi:10.1134/S0081543810020215Vladimir N. TemlyakovSP MAIK Nauka/InterperiodicaProceedings of the Steklov Institute of Mathematics...
DCG Dynamic Cost Greedy Algorithm DCG Dynamic Cardiogram (Holter Monitor) DCG Downward Characteristic Graph DCG Distance from the Center of Gravity (gliders) DCG Dot Com God DCG Dahl Creative Group (California) DCG Dallata Consulting Group Ltd (Etobicoke, ON, Canada) DCG Digital Code Generator Cop...
The assignment of tasks to slots can be done using a simple, online, greedy algorithm, e.g. assign each task to the slot with the earliest finishing time.Let μ=(Σ1=nnT1)/n and λ=max1 {T1} be the mean and maximum durations of the n tasks, respectively. The makespan of the ...
For the second method, we develop a greedy algorithm for selecting a given number of inequalities from the convex hull. Using these inequalities combined with Mixed-integer Linear Programming (MILP) technique, we propose an automatic method for evaluating the security of bit-oriented block ciphers ...
The main purpose of this report is to study the intimate relationship which appears to exist between lexicographic order on the one hand and linearity on the other. Topic headings include: The greedy algorithm, Linearity of lexicodes, Triangular lexicodes, Standard lexicodes, Gray lexicodes, Lexi...
Firstly, the FMO checkerboard pattern is used at the encoder, so as to prevent MBs of a large area getting lost. Then at the decoder, Greedy Spread Motion Region Extraction (GSMRE) method is used to distinguish low-motion region from high-motion region in each frame based on different ...
The basic precise greedy algorithm can be used to enumerate all possible partitions of all features to find the optimal segmentation point. When the amount of data is large, an approximate algorithm can be used. This can be used to obtain the percentile, propose n candidate segmentation points,...
The boosted decision tree model combines individual trees to reach a decision by using the technique of “boosting” in order to increase the accuracy of prediction. Boosting simply means that each succeeding tree is dependent on the preceding on. Hence, the algorithm or model learns by fitting ...
After a brief introduction of the theories of Extension Matrix and Rough Set, Greedy Algorithm, and Heuristic search Algorithm, this paper compares their performance in signal feature extraction applications. 简单介绍了基于扩张理论和粗集理论的优化算法、“贪心算法”和启发式搜索算法等典型特征选择的优化...