To measure the unique features of the proposed method, the k-means clustering learning algorithm based on the hybrid RBF-BP network (KMRBF-BP) is also compared with ILRBF-BP on artificial data sets.
Sugano et al. [1] take the cropped eye region as a point in a local manifold model and make gaze estimation by clustering learning samples with similar head poses and constructing their local manifold model.
Clustering learning and classification learning are two major tasks in pattern recognition.
When class information is available, fusing the advantages of both clustering learning and classification learning into a single framework is an important problem worthy of study.
To date, most algorithms generally treat clustering learning and classification learning in a sequential or two-step manner, i.e., first execute clustering learning to explore structures in data, and then perform classification learning on top of the obtained structural information.
And compared with the cluster learning algorithm, the correct rate of recognition increased by 5% when training samples fusion method was adopted.
The experiment demonstrated that HLSI can successfully cluster learning styles into three or four combinations based on learning performance, which suggests that the data mining technique can successfully explore multiple learning styles in problem-solving abilities.
Properties of Walktrap-GM are compared to those of several other approaches in Table 3, including heuristics for clustering, learning methods and parameter tuning.
The proposed algorithm produces better results when compared with Yang et al.'s [11], due to the directional clustered dictionary learning.
Since there has been no systematic summary on this topic, in this paper, we review the main-stream graph construction/learning methods involved in both general machine learning algorithms (including semi-supervised learning, clustering, manifold learning, and spectral kernel learning, etc).
