Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted
Greedoids: A structural framework for the greedy algorithm Progress in Combinatorial OptimizationW.R Pulleyblank (Ed.), Proceedings of the Silver Jubilee Conference on Combinatorics, Waterloo, June 1982, Academic Press, London/New York/San Francisco (1984), pp. 221-243 ...
The influence maximization problem has become one of the fundamental combinatorial optimization problems over the past decade due to its extensive applications in social networks. Although a \\(1-1/e\\) approximation ratio is easily obtained using a greedy algorithm for the submodular case, how to...
This model, unlike the classical ones, allows to investigate important algorithmic properties with respect to graphs of different densities. In particular the probabilistic performance of the greedy algorithm is analyzed in the case of the max-independent set and related problems....
Crucially, Requallo stops allocating tasks that reach completeness and the greedy MDP optimization can lead to “hard” tasks (those unlikely to reach completeness) being ignored in favor of “easy” tasks (those likely to reach completeness). (Note that a hard task in terms of completeness ...
Greedoids, a structural framework for the greedy algorithm W.R. Pulleyblank (Ed.), Progress in Combinatorial Optimization, Academic Press, New York (1984), pp. 221-243 View PDFView articleGoogle Scholar Kurosh, 1935 A. Kurosh Durchschnittsdarstellungen mit irredu...
We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as...
Similarly, ./active_learning/pareto_greedy/OMG_train_batch_1_chemprop_with_reaction_id.csv contains quantum chemistry calculation results from Round 1. This directory also contains 200 RDKit features (./rdkit_features) used in training ML models to overcome the local nature of message passing in...
Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Infor. theory 1993; 39(3): 930–945 4. Jones LK. A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training. Annals Statistics 1992...
according to a specific algorithm (that the program implements). In other words, it describes (for problems that can be solved by computer), how expensive is to solve the problem. Computational complexity is in the form of a function that typically takes thesize of the problem instanceas ...