Ensemble learning is a powerful machine learning paradigm which has exhibited apparent advantages in many applications. An ensemble in the context of machine learning can be broadly defined as a machine learning system that is constructed with a set of individual models working in parallel and whose outputs are combined with a decision fusion strategy to produce a single answer for a given problem. In this paper we introduce core of ensemble learning and key techniques to improve ensemble learning. Based on this we describe the procedure of two typical algorithms, i.e., adaboost and bagging, in detail. Finally we testify the superiority in classification accuracy with some experiments.

Image change detection is a research hotspot in many fields of application, such as environmental monitoring, disaster investigation, urban research, and more. How to reduce the influence of speckle noise when conducting change detection in an acquired SAR image is a challenging issue. This research shows that reasonably balancing noise suppression with the preservation ofthe edges of regions is the key to generating a good change map. Therefore, a new image detection method based on a selforganizing map and deep neural network (SOMDNCD) is proposed. First, the method uses a median filter to improve the difference image that is generated by the mean-ratio operator, which reduces the influence of the image point noise on generating difference maps. Compared with the difference map formed by the logarithmic ratio operator, the edge information in the image is excellently retained and the missed detection rate is reduced; second, the self-organizing map (SOM) network preprocesses the difference map, obtains a preliminary change map, and divides the pixels of the difference map into three types: no change, noise, and change. Finally, a deep neural network (DNN) is used to train a noise-like training set on the network to reduce the residual noise in the change class and obtain the final change graph. The experimental results show that compared with other current mainstream methods, the proposed SOMDNCD change detection method directly addressesnoise and is universal for a variety of datasets. The proposed method exhibits a lower missed detection rate in the synthetic aperture radar image dataset and a more ideal false alarm rate than other methods.

Evolutionary algorithms (EAs) are increasingly being applied to large-scale problems. MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. However, how to design high performance parallel EA based on MapReduce (MR-PEA) is still an open problem. In this paper, a parallel evolutionary algorithm model based on MapReduce by improving traditional parallel evolutionary algorithms model is proposed. The MR-PEA model is fit for large populations and datasets, has the characteristic of high scalable and efficiency. In order to justify the effectiveness of the MR-PEA model, we proposed a parallel gene expression programming based on MapReduce (MR-GEP) used to solve symbolic regression.