An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

Mikawa, Kenta; Ishida, Takashi; Goto, Masayuki

doi:10.7232/iems.2012.11.1.087

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Text mining is a knowledge discovery technology that enables researchers to discern patterns and trends based on unstructured text. It is possible to extract hidden knowledge using approaches such as natural language analysis, information retrieval, information extraction, and data mining [17,[39][40][41][42][43][44][45][46]. Patent documents contain lengthy and rich explanations in technical and legal terminologies [47,48].…”

Section: Topic Modeling Of Patentsmentioning

confidence: 99%

Identifying Emerging Trends of Financial Business Method Patents

Lee

Sohn

2017

Sustainability

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Financial technology has become an important part of the banking industry in recent times. This study attempts to propose a framework to identify emerging areas and trends using financial business method patents. Based on the abstracts of financial business method patents registered at the United States Patent and Trademark Office, this study first applies latent Dirichlet allocation to identify emerging topics. The probability of the annual occurrence of each topic is adjusted through the exponentially weighted moving average to reflect the importance of the recent probability of topics. Each topic is classified as "hot" or "cold" depending on whether the exponentially weighted moving average of the probabilities exceeds the threshold. We applied survival analysis to the time gap of recurrently becoming hot from a cold status with the associated factor of financial business method patents. The findings suggest that the topic with the short granted period and high forward citation is likely to become hot. In addition, the topic that is aged and specific in narrow areas is likely to continuously change into the hot or cold status. The approach proposed in this study contributes toward understanding topic emergence in the financial area and pursuing sustainable development.

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Section: Topic Modeling Of Patentsmentioning

confidence: 99%

Identifying Emerging Trends of Financial Business Method Patents

Lee

Sohn

2017

Sustainability

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“…det.M / D 1:In order to capture the specificities of each category c 1 ; : : : ; c N of documents,Mikawa et al [2012] extend the above work by learning N individual matrices M i . det.M / D 1:In order to capture the specificities of each category c 1 ; : : : ; c N of documents,Mikawa et al [2012] extend the above work by learning N individual matrices M i .…”

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Metric Learning

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Sebban³

2015

Synthesis Lectures on Artificial Intelligence and Machine Learn

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International audienceSimilarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval

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An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

Cited by 2 publications

References 13 publications

Identifying Emerging Trends of Financial Business Method Patents

Identifying Emerging Trends of Financial Business Method Patents

Metric Learning

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