Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

计量经济分析 = Econometric analysis

‘The Production of Space’, in: Frans Jacobi, Imagine, Space Poetry, Copenhagen, 1996, unpaginated.

The Production of Space

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

There is a growing recognition within the visual analytics community that interaction and inquiry are inextricable. It is through the interactive manipulation of a visual interface - the analytic discourse - that knowledge is constructed, tested, refined and shared. This article reflects on the interaction challenges raised in the visual analytics research and development agenda and further explores the relationship between interaction and cognition. It identifies recent exemplars of Visual analytics research that have made substantive progress toward the goals of a true science of interaction, which must include theories and testable premises about the most appropriate mechanisms for human-information interaction. Seven areas for further work are highlighted as those among the highest priorities for the next 5 years of visual analytics research: ubiquitous, embodied interaction; capturing user intentionality; knowledge-based interfaces; collaboration; principles of design and perception; interoperability; and interaction evaluation. Ultimately, the goal of a science of interaction is to support the visual analytics and human-computer interaction communities through the recognition and implementation of best practices in the representation and manipulation of visual displays.

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The science of interaction

The world's corpora of data grow in size and complexity every day, making it increasingly difficult for experts to make sense out of their data. Although machine learning offers algorithms for finding patterns in data automatically, they often require algorithm-specific parameters, such as an appropriate distance function, which are outside the purview of a domain expert. We present a system that allows an expert to interact directly with a visual representation of the data to define an appropriate distance function, thus avoiding direct manipulation of obtuse model parameters. Adopting an iterative approach, our system first assumes a uniformly weighted Euclidean distance function and projects the data into a two-dimensional scatterplot view. The user can then move incorrectly-positioned data points to locations that reflect his or her understanding of the similarity of those data points relative to the other data points. Based on this input, the system performs an optimization to learn a new distance function and then re-projects the data to redraw the scatter-plot. We illustrate empirically that with only a few iterations of interaction and optimization, a user can achieve a scatterplot view and its corresponding distance function that reflect the user's knowledge of the data. In addition, we evaluate our system to assess scalability in data size and data dimension, and show that our system is computationally efficient and can provide an interactive or near-interactive user experience.

/pdf/dis-function-learning-distance-functions-interactively-2ht9sy41ib.pdf

Dis-function: Learning distance functions interactively

Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.

iPCA: an interactive system for PCA-based visual analytics

Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n=1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.

https://www.cs.tufts.edu/~remco/publications/2014/InfoVis2014-JND.pdf

Ranking visualizations of correlation using Weber's law

In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client-server architecture, where the user interacts with a lightweight client-side interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset. We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.

https://dl.acm.org/doi/pdf/10.1145/2882903.2882919

Remco Chang

Papers

The science of interaction

Dis-function: Learning distance functions interactively

iPCA: an interactive system for PCA-based visual analytics

Ranking visualizations of correlation using Weber's law

Dynamic Prefetching of Data Tiles for Interactive Visualization