文件名称：Knowledge Transfer between Computer Vision and Text Mining: Similarity-based
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更新时间：2021-11-18 13:28:49
迁移学习 计算机视觉 文本挖掘
Machine learning is currently a vast area of research with applications in a broad range of fields such as computer vision, bioinformatics, information retrieval, natural language processing, audio processing, data mining, and many others. Among the variety of state-of-the-art machine learning approaches for such applications are the similarity-based learning methods. Learning based on similarity refers to the process of learning based on pairwise similarities between the training samples. The similarity-based learning process can be both supervised and unsupervised, and the pairwise relationship can be either a similarity, a dissimilarity, or a distance function. This book studies several similarity-based learning approaches, such as nearest neighbor models, local learning, kernel methods, and clustering algorithms. A nearest neighbor model based on a novel dissimilarity for images is presented in this book. It is used for handwritten digit recognition and achieves impressive results. Kernel methods are used in several tasks investigated in this book. First, a novel kernel for visual word histograms is presented. It achieves state-of-the-art performance for object recognition in images. Several kernels based on a pyramid representation are presented next. They are used for facial expression recognition from static images. The same pyramid representation is successfully used for text categorization by topic. Moreover, an approach based on string kernels for native language identification is also presented in this work. The approach achieves state-of-the-art performance levels, while being language independent and theory neutral. An interesting pattern can already be observed, namely that the machine learning tasks approached in this book can be divided into two different areas: computer vision and string processing. Despite the fact that computer vision and string processing seem to be unrelated fields of study, image analysis and string processing are in some ways similar. As will be shown by the end of this book, the concept of treating image and text in a similar fashion has proven to be very fertile for specific applications in computer vision. In fact, one of the state-of-the-art methods for image categorization is inspired by the bag of words representation,