Meta-aprendizado para otimizacao de parametros de redes neurais

07/10/2021
by   Tarsicio Lucas, et al.
0

The optimization of Artificial Neural Networks (ANNs) is an important task to the success of using these models in real-world applications. The solutions adopted to this task are expensive in general, involving trial-and-error procedures or expert knowledge which are not always available. In this work, we investigated the use of meta-learning to the optimization of ANNs. Meta-learning is a research field aiming to automatically acquiring knowledge which relates features of the learning problems to the performance of the learning algorithms. The meta-learning techniques were originally proposed and evaluated to the algorithm selection problem and after to the optimization of parameters for Support Vector Machines. However, meta-learning can be adopted as a more general strategy to optimize ANN parameters, which motivates new efforts in this research direction. In the current work, we performed a case study using meta-learning to choose the number of hidden nodes for MLP networks, which is an important parameter to be defined aiming a good networks performance. In our work, we generated a base of meta-examples associated to 93 regression problems. Each meta-example was generated from a regression problem and stored: 16 features describing the problem (e.g., number of attributes and correlation among the problem attributes) and the best number of nodes for this problem, empirically chosen from a range of possible values. This set of meta-examples was given as input to a meta-learner which was able to predict the best number of nodes for new problems based on their features. The experiments performed in this case study revealed satisfactory results.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

07/25/2019

Towards meta-learning for multi-target regression problems

Several multi-target regression methods were devel-oped in the last year...
10/24/2020

Modeling and Optimization Trade-off in Meta-learning

By searching for shared inductive biases across tasks, meta-learning pro...
06/17/2021

Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error

Calibration of neural networks is a topical problem that is becoming inc...
07/17/2020

A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems

The exponential growth of volume, variety and velocity of data is raisin...
03/09/2021

u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems

Collaborative Filtering (CF) has become the standard approach to solve r...
07/15/2021

A Channel Coding Benchmark for Meta-Learning

Meta-learning provides a popular and effective family of methods for dat...
12/18/2019

Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems

The effectiveness of recommender system algorithms varies in different r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.