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Focusing on non-iterative approaches in training feed-forward neural networks, this special issue includes 12 papers to share the latest progress, current challenges, and potential applications of this topic. This editorial presents a background of the special issue and a brief introduction to the 12 contributions. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low learning efficiency and slow convergence speed when mobile robot uses reinforcement learning method for path planning in complex environment, a reinforcement learning method based on each round path planning result is proposed. Firstly, the algorithm adds obstacle learning matrix to improve the success rate of path planning; and introduces heuristic reward to speed up the learning process by reducing the search space; then proposes a method of dynamically adjusting the exploration factor to balance the exploration and utilization in path planning, so as to further improve the performance of the algorithm. Finally, the simulation experiment in grid environment shows that compared with Q-learning algorithm, the improved algorithm not only shortens the average path length of the robot to reach the target position, but also speeds up the learning efficiency of the algorithm, so that the robot can find the optimal path more quickly. The code of EPRQL algorithm proposed in this paper has been published to GitHub: https://github.com/panpanpanguoguoqian/mypaper1.git. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

Learning from categorical data is a critical yet challenging task. Current research focuses on either leveraging the complex interaction between and within categorical values to generate a numerical representation, or designing a model that can tackle this types of data directly. However, both of these paradigms overlook the relation between the data characteristics and learning model hypothesis. In this paper, we propose a model-aware categorical data embedding framework that jointly reveals the intrinsic categorical data characteristics and optimizes the fitness of the representation for the follow-up learning model. An ELM-aware and a SVM-aware representation methods have been instantiated under this framework. Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Incremental Extreme Learning Machine (I-ELM) is a typical constructive feed-forward neural network with random hidden nodes, which can automatically determine the appropriate number of hidden nodes. However I-ELM and its variants suffer from a notorious problem, that is, the input parameters of these algorithms are randomly assigned and kept fixed throughout the training process, which results in a very unstable performance of the model. To solve this problem, we propose a novel Back-Propagation ELM (BP-ELM) in this study, which can dynamically assign the most appropriate input parameters according to the current residual error of the model during the increasing process of the hidden nodes. In this way, BP-ELM can greatly improve the quality of newly added nodes and then accelerate the convergence rate and improve the model performance. Moreover, under the same error level, the network structure of the model obtained by BP-ELM is more compact than that of the I-ELM. We also prove the universal approximation ability of BP-ELM in this study. Experimental results on three benchmark regression problems and a real-life traffic flow prediction problem empirically show that BP-ELM has better stability and generalization ability than other I-ELM-based algorithms. [ABSTRACT FROM AUTHOR]

This work proposes a new algorithm for training neural networks to solve the problems of feature selection and function approximation. The algorithm applies different weight constraint functions for the hidden and the output layers of a multilayer perceptron neural network. The LASSO operator is applied to the hidden layer; therefore, the training provides automatic selection of relevant features and the standard norm regularization function is applied to the output layer. Therefore, we propose a multi-objective training algorithm that is able to select the important features while solving the approximation problem. [ABSTRACT FROM AUTHOR]

Robo-advisors are a class of robots based on the financial needs of investors, through the algorithm and products to complete the previous financial advisory services provided by human intervention. They provide financial advice based on machine learning algorithms. However, many of the previous general algorithms are less suitable for information fusion in heterogeneous data. We propose an incremental multiple kernel extreme learning machine (IMK-ELM) model, which initializes a generic training database and then tunes itself to the classification task. Our IMK-ELM simultaneously updates the training dataset as well as the weights used to combine multiple information sources. We demonstrate our system on a financial recommendation problem in BCSs. We analyze the behavior of the algorithm, comparing its performance and scaling properties to other state-of-the-art approaches. Experimental results demonstrate that the proposed method appropriately solves a wide range of classification problems and is able to efficiently deal with large-scale tasks like Robo-advisors. [ABSTRACT FROM AUTHOR]