Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ferns ensembles offer an accurate and efficient multiclass non-linear classification, commonly at the expense of consuming a large amount of memory.  introduce a two-fold contribution that produces large reductions in their memory consumption. First, an efficient L0 regularised cost optimisation finds a sparse representation of the posterior probabilities in the ensemble by discarding elements with zero contribution to valid responses in the training samples. As a by-product this can produce a prediction accuracy gain that, if required, can be traded for further reductions in memory size and prediction time. Secondly, posterior probabilities are quantised and stored in a memory-friendly sparse data structure.  reported a minimum of 75% memory reduction for different types of classification problems using generative and discriminative ferns ensembles, without increasing prediction time or classification error. For image patch recognition our proposal produced a 90% memory reduction, and improved in several percentage points the prediction accuracy.
['Antonio L. Rodriguez', 'Vítor Sequeira']
I am writing a research paper on a idea Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities
[{"from": "human", "value": "I am writing a research paper on a idea Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ferns ensembles offer an accurate and efficient multiclass non-linear classification, commonly at the expense of consuming a large amount of memory.  introduce a two-fold contribution that produces large reductions in their memory consumption. First, an efficient L0 regularised cost optimisation finds a sparse representation of the posterior probabilities in the ensemble by discarding elements with zero contribution to valid responses in the training samples. As a by-product this can produce a prediction accuracy gain that, if required, can be traded for further reductions in memory size and prediction time. Secondly, posterior probabilities are quantised and stored in a memory-friendly sparse data structure.  reported a minimum of 75% memory reduction for different types of classification problems using generative and discriminative ferns ensembles, without increasing prediction time or classification error. For image patch recognition our proposal produced a 90% memory reduction, and improved in several percentage points the prediction accuracy."}]