Casting Random Forests as Artificial Neural Networks (and Profiting from It)
نویسنده
چکیده
While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the majority of the UCI datasets used for benchmarking.
منابع مشابه
Prediction the Return Fluctuations with Artificial Neural Networks' Approach
Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study incl...
متن کاملMapping Dieback Intensity Distribution in Zagros Oak Forests Using Geo-statistics and Artificial Neural Network
The first and most important issue in forest drought management is knowledge of the location and severity of forest decline. In this regard, we used geostatistics and artificial neural network methods to map the dieback intensity of oak forests in the Ilam province, Iran. We used a systematic random sampling with a 250 × 200 m grid to establish 100 plots, each covering 1200 m2. The percentage ...
متن کاملApplication of artificial neural networks on drought prediction in Yazd (Central Iran)
In recent decades artificial neural networks (ANNs) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. This paper presents the application of artificial neural networks to predict drought in Yazd meteorological station. In this research, different archite...
متن کاملArtificial neural networks: applications in predicting pancreatitis survival
Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...
متن کاملPrediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks
The artificial neural networks, the learning algorithms and mathematical models mimicking the information processing ability of human brain can be used non-linear and complex data. The aim of this study was to predict the breeding values for milk production trait in Iranian Holstein cows applying artificial neural networks. Data on 35167 Iranian Holstein cows recorded between 1998 to 2009 were ...
متن کامل