Titlebook: Knowledge Transfer between Computer Vision and Text Mining; Similarity-based Lea Radu Tudor Ionescu,Marius Popescu Book 2016 Springer Inter

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Motivation and Overview,This chapter gives a brief overview of machine learning and related fields of study. The concept of treating image and text in a similar fashion is then presented. A few successful examples of knowledge transfer between computer vision and text mining are also given. The chapter ends with a full overview of the organization of this book.

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ConclusionsThis chapter presents the general conclusions of this book. The conclusions point to the fact that the concept of treating image and text in a similar way is indeed fertile. Some advice on future work and new directions that could arise from this concept is also provided.

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State-of-the-Art Approaches for String and Text Analysis work belong to two major scientific fields, namely computational biology and text mining. Consequently, the chapter is divided into two separate sections corresponding to the two fields of study. First, clustering methods used for phylogenetic analysis and other methods for sequencing and comparing

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Local Rank Distanceneral principles than rank distance, but it is also developed having a practical motivation in mind, specifically to be more suitable for DNA strings or text. The chapter shows three applications of LRD. The first application is the phylogenetic analysis of mammals. Experiments show that phylogeneti

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