Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AdaBoost is an algorithm with a procedure of selecting the data events from a dataset at each iteration sequence. The data events are selected stochastically using a random number generator. , a deterministic AdaBoost algorithm is proposed in contrast to the usual stochastic one. For doing this  derive the modified Fisherpsilas formulas moderated to the deterministic method. These formulas contain a scheme to treat data set with ight vector. To verify the performance of proposed algorithm,  compare with the results of different measurements with the deterministic and the stochastic method, by gradually increasing the prune rate and the number of ak learner in the network structure. Through the result of these experiments,  show that our proposed method has higher performance than typical stochastic one.
['Jong Chan Lee', 'Wu Jun', 'Won Don Lee']
I am writing a research paper on a idea Deterministic AdaBoost algorithm based on FLDF
[{"from": "human", "value": "I am writing a research paper on a idea Deterministic AdaBoost algorithm based on FLDF"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AdaBoost is an algorithm with a procedure of selecting the data events from a dataset at each iteration sequence. The data events are selected stochastically using a random number generator. , a deterministic AdaBoost algorithm is proposed in contrast to the usual stochastic one. For doing this  derive the modified Fisherpsilas formulas moderated to the deterministic method. These formulas contain a scheme to treat data set with ight vector. To verify the performance of proposed algorithm,  compare with the results of different measurements with the deterministic and the stochastic method, by gradually increasing the prune rate and the number of ak learner in the network structure. Through the result of these experiments,  show that our proposed method has higher performance than typical stochastic one."}]