Titlebook: Computational Intelligence; 11th International J Juan Julián Merelo,Jonathan Garibaldi,Kurosh Madan Conference proceedings 2021 Springer Na

Forage飼料

Niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Sc and by comparing the different methods in a larger experimental setup. The results show that feature selection can generate better rules in most of the cases while also being more efficient to in a production environment.

態(tài)度暖昧

Correlation Analysis Via Intuitionistic Fuzzy Modal and Aggregation Operatorsity and possibility modal operators along with intuitionistic fuzzy t-norms and t-conorms are investigated by verifying the conditions under which A-CC preserve the main properties related to conjugate and complement operations performed on A-IFS.

斷言

遺傳

Towards a Class-Aware Information Granulation for Graph Embedding and Classificationormance improvements when considering also the ground-truth class labels in the information granulation procedure. Furthermore, since the granulation procedure is based on random walks, it is also very appealing in Big Data scenarios.

homeostasis

Deep Convolutional Neural Network Processing of Images for Obstacle Avoidancein the lab by a human operator. The network learned the correct responses of left, right, or straight for each of the images with a very low error rate when checked on test images. In addition, ten tests on the actual robot showed that it could successfully and consistently drive through the lab while avoiding obstacles.

companion

STERN

兇猛

CREST

https://doi.org/10.1007/978-3-642-69591-9n opens doors for a sampling version of the algorithm, which we call CVaR Q-learning. In order to allow optimizing CVaR on large state spaces, we also formulate loss functions that are later used in a deep learning context. Proposed methods are theoretically analyzed and experimentally verified.

contrast-medium

CVaR Q-Learningn opens doors for a sampling version of the algorithm, which we call CVaR Q-learning. In order to allow optimizing CVaR on large state spaces, we also formulate loss functions that are later used in a deep learning context. Proposed methods are theoretically analyzed and experimentally verified.