AdaNet (Adaptive structural learning of artificial neural Networks) is a neural architecture search framework based on Boosting ensemble learning, which can create high-quality models through integrated subnets. The difference between subnets generated by the existing AdaNet is not significant, which limits the reduction of generalization error in ensemble learning. In the two steps of AdaNet:setting subnet network weights and integrating subnets, Adagrad, RMSProp (Root Mean Square Prop), Adam, RAdam (Rectified Adam) and other adaptive learning rate methods were used to improve the existing optimization algorithms in AdaNet. The improved optimization algorithms were able to provide different degrees of learning rate scaling for different dimensional parameters, resulting in a more dispersed weight distribution, so as to increase the diversity of subnets generated by AdaNet, thereby reducing the generalization error of ensemble learning. The experimental results show that on the three datasets:MNIST (Mixed National Institute of Standards and Technology database), Fashion-MNIST and Fashion-MNIST with Gaussian noise, the improved optimization algorithms can improve the search speed of AdaNet, and more diverse subnets generated by the method can improve the performance of the ensemble model. For the F1 value, which is an index to evaluate the model performance, compared with the original method, the improved methods have the largest improvement of 0.28%, 1.05% and 1.10% on the three datasets.

In order to meet the needs of precise positioning of the mobile robot, a fingerprint positioning method of particle filter based on different resampling algorithms was presented. Firstly, during the positioning phase, the motion model built on robot kinematics served as the proposal density of particle filter, and the observation information and environment fingerprint were infused into the filtering process to enhance the particles' refining capacity and reduce the required number of particles. Secondly, an Exquisite Resampling (ER) algorithm was introduced to improve the refining ability of the particles, thus the effect of particle impoverishment could be decreased and the localization accuracy could be improved. At last, the influence of the positioning accuracy caused by different re-sampling algorithms was analyzed, and a further investigation on the accuracy and reliability of localization algorithm from different experimental perspectives was given. The experimental results show that this algorithm has the advantages in localization accuracy and robustness.

M-ary Support Vector Machine (M-ary SVM) for multi-category classification has the advantage of simple structure, but the disadvantage of weak generalization ability. This paper presented an enhanced M-ary SVM algorithm in combination with error correction coding theory. The main idea of the approach was to generate a group of best codes based on information codes derived from the original category flags information, then utilize such codes as the basis for training the classifier, while in the final feed-forward phase the output codes composed of each sub-classifier could be corrected by error detection and correction principle if there exists any identifying error. The experimental results confirm the effectiveness of the improved algorithm brought about by introducing as few sub-classifiers as possible.