On Method Overfitting
نویسنده
چکیده
Benchmark problems should be hard. True. Methods for solving problems should be useful for more than just “beating” a particular benchmark. Truer still, we believe. In this paper, we examine the worth of the approach consisting of concentration on a particular set of benchmark problems, an issue raised by a recent paper by Ian Gent. We find that such a methodology can easily lead to publications of limited general use as far as our ability to solve practical problems is concerned.
منابع مشابه
Avoiding overfitting in multilayer perceptrons with feeling-of-knowing using self-organizing maps.
Overfitting in multilayer perceptron (MLP) training is a serious problem. The purpose of this study is to avoid overfitting in on-line learning. To overcome the overfitting problem, we have investigated feeling-of-knowing (FOK) using self-organizing maps (SOMs). We propose MLPs with FOK using the SOMs method to overcome the overfitting problem. In this method, the learning process advances acco...
متن کاملComplex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Recent systems on structured prediction focus on increasing the level of structural dependencies within the model. However, our study suggests that complex structures entail high overfitting risks. To control the structure-based overfitting, we propose to conduct structure regularization decoding (SR decoding). The decoding of the complex structure model is regularized by the additionally train...
متن کاملStacked Training for Overfitting Avoidance in Deep Networks
When training deep networks and other complex networks of predictors, the risk of overfitting is typically of large concern. We examine the use of stacking, a method for training multiple simultaneous predictors in order to simulate the overfitting in early layers of a network, and show how to utilize this approach for both forward training and backpropagation learning in deep networks. We then...
متن کاملAn Approach to Reducing Overfitting in FCM with Evolutionary Optimization
Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the...
متن کاملAvoiding Parameter Overfitting in a Monte Carlo Approach to Real-Time Strategy Games
This project examines overfitting and generalization error with Monte Carlo Tree Search in the real-time strategy video game StarCraft and provides a basis for evaluating the specific scenarios of StarCraft based on their estimated generalization error. How can we tell if we are overfitting the input parameters to specific scenarios in StarCraft? How well does a specific scenario of StarCraft g...
متن کاملModeling of Compressive Strength of Metakaolin Based Geopolymers by The Use of Artificial Neural Network RESEARCH NOTE)
In order to study the effect of R2O/Al2O3 (where R=Na or K), SiO2/Al2O3, Na2O/K2O and H2O/R2O molar ratios on the compressive strength (CS) of Metakaolin base geopolymers, more than forty data were gathered from literature. To increase the number of data, some experiments were also designed. The resulted data were utilized to train and test the three layer artificial neural network (ANN). Bayes...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- J. Heuristics
دوره 4 شماره
صفحات -
تاریخ انتشار 1998