Parameter estimation is generally difficult, requiring advanced methods such as the expectation-maximization (EM). This chapter focuses on the ideas behind EM, rather than its complex mathematical properties or proofs. We use the Gaussian mixture model (GMM) as an illustrative example to find what leads us to the EM algorithms, e.g., complete and incomplete data likelihood, concave and nonconcave loss functions, and observed and hidden variables. We then derive the EM algorithm in general and its application to GMM.

This chapter presents a simple but working face recognition system, which is based on the nearest neighbor search algorithm. Albeit simple, it is a complete pattern recognition pipeline. We can then examine every component in it, and analyze potential difficulties and pitfalls one may encounter. Furthermore, we introduce a problem-solving framework, which will be useful in the rest of this book and in solving other tasks.

This chapter is not about one particular method (or a family of methods). Instead, it provides a set of tools useful for better pattern recognition, especially for real-world applications. They include the definition of distance metrics, vector norms, a brief introduction to the idea of distance metric learning, and power mean kernels (which is a family of useful metrics). We also establish by examples that proper normalizations of our data are essential, and introduce a few data normalization and transformation methods.

Starting from this chapter, Part III introduces several commonly used algorithms in pattern recognition and machine learning. Support vector machines (SVM) starts from a simple and beautiful idea: large margin. We first show that in order to find such an idea, we may need to simplify our problem setup by assuming a linearly separable binary one. Then we visualize and calculate the margin to reach the SVM formulation, which is complex and difficult to optimize. We practice the simplification procedure again until the formulation becomes viable, briefly mention the primal--dual relationship, but do not go into details of its optimization. We show that the simplification assumptions (linear, separable, and binary) can be relaxed such that SVM will solve more difficult tasks---and the key ideas here are also useful in other tasks: slack variables and kernel methods.

Information theory is developed in the communications community, but it turns out to be very useful for pattern recognition. In this chapter, we start with an example to develop the ideas of uncertainty and its measurement, i.e., entropy. A few core results in information theory are introduced: entropy, joint and conditional entropy, mutual information, and their relationships. We then move to differential entropy for continuous random variables and find distributions with maximum entropy under certain constraints, which are useful for pattern recognition. Finally, we introduce the applications of information theory in our context: maximum entropy learning, minimum cross entropy, feature selection, and decision trees (a widely used family of models for pattern recognition and machine learning).

The Japanese are multireligious, non-religious or neither, depending upon how religiosity is defined. This chapter endeavors to make sense of Japanese religiosity and to unravel the ways in which it has formed an undercurrent in Japanese society. The first section focuses on the characteristics of traditional religions which took hold in premodern Japan: Shinto, Japan’s native religion, and two imported religions: Buddhism and Christianity. The second section analyzes newer religions that were founded in the twentieth century and scrutinizes the more recent emergence of a cultural trend in which individuals seek forms of spirituality outside of established religious spheres. The third section looks at this-worldly financial and political activities of these old and new religions, and the fourth sketches how the general trend of secularization faces the revitalization of religious practices.

Two ostensibly contradictory forces operate in Japanese society, as is the case in other industrialized societies. On the one hand, it is subject to many centrifugal forces that tend to diversify its structural arrangements, lifestyles, and value orientations. On the other hand, a range of centripetal forces drives Japanese society towards homogeneity and uniformity. This chapter endeavors to recapitulate these two forces in the context of Japan’s civil society. The first section examines the fragmentation of social relations. The second section scrutinizes the rise of social movements in the 2010s. The third section delves into the quiet spread of volunteer activities and non-profit organizations and non-governmental organizations as the backdrop of the dissenting protests and the changing configuration of interest groups at large. The fourth section examines the viability of the emic notion analogous to citizenship in the analysis of the Japanese context. The last section attempts to locate a variety of forms of control in an analytical framework and to summarize their features as ‘friendly authoritarianism’ across the wide spectrum of Japanese society.

The international evaluation of Japan’s work practice has made an about-turn since the beginning of the twenty-first century, from role model to problem case. The general response has shifted from admiration to caution and from envy to skepticism. Once heralded as a model from which every country must learn, the nation’s business world now appears to be seen as a framework to be avoided. Yet, Japan remains the third largest economy in the world, even if its potentials and challenges are currently being overshadowed by the struggle for international hegemony between the two superpowers – the United States and China. This chapter illustrates the plurality in Japan's world of labor by first highlighting the continuation of the old patterns: the prevailing culture of small business and the perpetuation of the so-called Japanese-style management model. The second half of the chapter focuses on the emerging changes by examining the growing spread of a new form of capitalism, which one might call 'cultural capitalism', based on the knowledge industry and the production and consumption of symbols, images, and representations.

This chapter investigates the sampling issue of the social sciences in the analysis of contemporary Japanese society, addressing the basic question of why some groups are overrepresented in its portrayal while others tend to be disregarded, despite its pluralistic realities. In doing so, it then identifies four models: monocultural, multiethnic, multiclass and multicultural. It also highlights three areas in which the particular case of Japan has something to offer to social science issues in general – the convergence debate, cultural relativism, and the distinction between ideologies and lived realities in the description of a given society – and demonstrates why it is necessary to be sensitive to two types of relativism: intrasocietal and intersocietal.