Titlebook: Artificial Intelligence and Soft Computing; 22nd International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2023

发现

Application of Monte Carlo Algorithms with Neural Network-Based Intermediate Area to the Thousand Cae on the part of computer players. The main goal of this study is to create modern artificial intelligence algorithms based on neural networks and algorithms based on the Monte Carlo method to control players in the popular card game called Thousand. We propose two approaches based on neural network

Nonconformist

Infusion

Fuzzy Hyperplane Based K-SVCR Multi-class Classification with Its Applications to Stock Prediction Pation that is called a fuzzy hyperplane-based support vector classification regression for .-class classification (FH-.-SVCR). The main characteristics of the proposed FH-.-SVCR are that it assigns fuzzy membership degrees to every data vector according to the importance and the parameters for the h

欢乐东方

寻找

Matrimony

The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers approaches such as “Tangent Learning Vector Quantization" and “Tangent Distance Kernel for Support Vector Machines" for classification of data. These models assume that there are class invariant manifolds that can be locally approximated by an affine space of similar dimensions. However, in practic

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纵欲

Viscosity Estimation of Water-PVP Solutions from Droplets Using Artificial Neural Networks and Imagein many industrial areas, such as the chemical, pharmaceutical, and energy-related industries. Capillary viscometers are the most used instrument for measuring viscosity. Still, they are expensive and complex, which represents a challenge in industries where accurate and real-time viscosity knowledg

发誓放弃

Commemorate

Unsupervised Representation Learning: Target Regularization for Cross-Domain Sentiment Classificatioassification problem. Finding domain invariant feature representations is a transfer learning method for transmitting knowledge between source and target domain data. Our method aims to avoid the overfitting of an autoencoder model on source domain training data in a trained embedded feature space u