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 multiple times. The input datasets were discretised with several algorithms representing ...
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 ...
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...
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...
The closest approximation to π using three terms of the greedy Egyptian fraction algorithm. It is somewhat more accurate than this one but requires a larger denominator term. 3.141592653589... = π (pi) (π (pi), the best-known irrational constant) ...
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 ...
expected, the algorithm tended to change positions that were variable rather than conserved in the multiple sequence alignment (Fig.2C, Supplementary Fig.S4A, and Supplementary Fig.S5). Although the algorithm did make mutations to residues in the core of the protein (Table in Fig.2B), the ...
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...
A greedy algorithm for block-sparse vectors is proposed in Ref. [30]. The atomic norm of hierarchically sparse vectors is the ℓ1-norm – hence, the analogous strategy does not directly carry over. Instead, hierarchical sparse vectors were introduced in a line of work [20], [37], [38...
The weighted quantile sketch algorithm greatly speeds up the training speed by establishing candidate partition nodes for each feature in advance, instead of using the original greedy algorithm to traverse all values of each feature. The sparse-aware algorithm can automatically process the missing ...