Δεν σας αρέσει; Δεν πειράζει! Μπορείτε αν θέλετε να κάνετε επιστροφή εντός 30 ημερών.
Δεν θα κάνετε ποτέ λάθος με μια δωροεπιταγή. Χαρίστε στους αγαπημένους σας την επιλογή να διαλέξουν οι ίδιοι οτιδήποτε από τη συλλογή μας.
30 ημέρες για την επιστροφή των προϊόντων
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems. TOC:Part I. Multi-objective Feature Selection.- Part II. Multi-objective Model Selection for Better Accuracy.- Part III. Multi-objective Learning for Better Interpretability.- Part IV. Generation of Ensembles using Multi-objective Optimization.- Part V. Applications of Multi-objective Machine Learning.