## 2 Summary of the Contributions
### Basic Problems of ML  
Paper [25] deals with the problem of instance selection for classifiers. The main goal is to improve the performance of a classifier (its speed and accuracy) by eliminating redundant and noisy samples. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3.  
Support vector machines are a well-known classifiers due to their superior classification performance. To decrease the large-scale SVM complexity, in [26], a novel data reduction method for reducing the training time by combining decision trees and relative support distance is proposed. The method selects good support vector candidates in each partition generated by the decision trees. The selected candidates reduced the training time while maintaining good classification performance in comparison with existing approaches  
Paper [27] deals with the problem of solving of partial differential equations, which is a hot topic of mathematical research. The authors introduce an improved Physics Informed Neural Network (PINN) for solving partial differential equations. PINN takes the physical information that is contained in partial differential equations as a regularization term, which improves a performance of NNs. The experimental results show that PINN is effective in solving partial differential equations and deserves further research.  
Machine learning of automata and grammars has a wide range of applications in such fields as syntactic pattern recognition, computational biology, systems modeling, natural language acquisition, and knowledge discovery. In [28], an approach to non-deterministic finite automaton inductive synthesis that is based on answer set programming (ASP) solvers are proposed. They consist of preparing logical rules before starting the searching process. The authors show how the proposed ASP solvers help to tackle the regular inference problem for large-size instances and compare their approach with the existing ones. Experiments indicated that the proposed approach clearly outperforms the current state-of-the-art satisfiability-based method and all backtracking algorithms proposed in the literature.  
Paper [29] sits in the scientific field known as grammatical inference (GI), automata learning, grammar identification, or grammar induction. The matter under consideration is the set of rules that lie behind a given sequence of words and the main task is to discover the rules that can help to evaluate new, unseen words. The authors propose a new grammatical inference method and applied it to a real bioinformatics task, i.e., classification of amyloidogenic sequences. In the experimental evaluation, they showed that the new grammatical inference algorithm gives the best results in comparison to other automata or grammar learning methods as well as ML approach combining an unsupervised data-driven distributed representation and SVM.  
Paper [30] is about anticipatory classifier systems, i.e., the classifier systems that learn by using a cognitive mechanism of anticipatory behavioral control which was introduced in cognitive psychology. The authors note that the learning classifier systems revealed many real-world sequential decision problems where the preferred objective is the maximization of the average of successive rewards. To address such problems, they proposes a modification toward a learning component: a new average reward criterion. In the experimental study, they showed that the anticipatory classifier systems with an averaged reward criterion can be used successively in multi-step environments.