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

Time-series clustering - A decade review

Contents: J. Gale, Preface. Part I:Radical Constructivism and Social Constructionism. E. von Glasersfeld, A Constructivist Approach to Teaching. K.J. Gergen, Social Construction and the Educational Process. J. Shotter, In Dialogue: Social Constructionism and Radical Constructivism. J. Richards, Construct[ion/iv]ism: Pick One of the Above. Part II:Information-Processing Constructivism and Cybernetic Systems. F. Steier, From Universing to Conversing: An Ecological Constructionist Approach to Learning and Multiple Description. R.J. Spiro, P.J. Feltovich, M.J. Jacobson, R.L. Coulson, Cognitive Flexibility, Constructivism, and Hypertext: Random Access Instruction for Advanced Knowledge Acquisition in Ill-Structured Domains. K. Tomm, Response to Chapters by Spiro et al. and Steier. P.W. Thompson, Constructivism, Cybernetics, and Information Processing: Implications for Technologies of Research on Learning. Part III:Social Constructivism and Sociocultural Approaches. H. Bauersfeld, The Structuring of the Structures: Development and Function of Mathematizing as a Social Practice. J.V. Wertsch, C. Toma, Discourse and Learning in the Classroom: A Sociocultural Approach. C. Konold, Social and Cultural Dimensions of Knowledge and Classroom Teaching. J. Confrey, How Compatible Are Radical Constructivism, Sociocultural Approaches, and Social Constructivism? Analysis and Synthesis I: Alternative Epistemologies. M.H. Bickhard, World Mirroring Versus World Making: There's Gotta Be a Better Way. Part IV:Alternative Epistemologies in Language, Mathematics, and Science Education. R. Duit, The Constructivist View: A Fashionable and Fruitful Paradigm for Science Education Research and Practice. G.B. Saxe, From the Field to the Classroom: Studies in Mathematical Understanding. N.N. Spivey, Written Discourse: A Constructivist Perspective. T. Wood, From Alternative Epistemologies to Practice in Education: Rethinking What It Means to Teach and Learn. E. Ackermann, Construction and Transference of Meaning Through Form. D. Rubin, Constructivism, Sexual Harassment, and Presupposition: A (Very) Loose Response to Duit, Saxe, and Spivey. Part V:Alternative Epistemologies in Clinical, Mathematics, and Science Education. E. von Glasersfeld, Sensory Experience, Abstraction, and Teaching. R. Driver, Constructivist Approaches to Science Teaching. T. Wood, P. Cobb, E. Yackel, Reflections on Learning and Teaching Mathematics in Elementary School. P. Lewin, The Social Already Inhabits the Epistemic: A Discussion of Driver Wood, Cobb, and Yackel and von Glasersfeld. J. Becker, M. Varelas, Assisting Construction: The Role of the Teacher in Assisting the Learner's Construction of Preexisting Cultural Knowledge. E.H. Auerswald, Shifting Paradigms: A Self-Reflective Critique. Analysis and Synthesis II: Epsitemologies in Education. P. Ernest, The One and the Many. Analysis and Synthesis III: Retrospective Comments and Future Prospects. L.P. Steffe, Alternative Epistemologies: An Educator's Perspective. J. Gale, Epilogue.

教育中的建构主义 = Constructivism in Education

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

There are several studies about extended classification system (XCS) in past years. XCS model can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation to apply XCS to global asset allocation in the country-specific exchanged traded funds (ETFs). Since international stock price trend is influenced by unknown and unpredictable surroundings, using XCS to model the fluctuations on global financial market allows for the discovery of the patterns of the future trends. As such, the benefits of international asset diversification can be achieved in a tax-efficient way with country-specific ETFs at a low transaction cost with minimized tracking error. These empirical results indicate that XCS is capable of evolving over time, and thus XCS can provide a good indicator for future global asset allocation decision-making aiming at maximized profit.

https://ir.nctu.edu.tw:443/bitstream/11536/32239/1/000278424600057.pdf

Strategy of global asset allocation using extended classifier system

The empirical research on the financial trading market in the past few years is quite important and complex. In addition to discover the characteristics of the market trend movement, that buying and selling timing in the market has a great impact on profitability performance. Therefore, through the extension study of previously published literature, there should be existing relatively low risk buying and selling points in the trading market. Using market profile indicators and financial engineering physical quantities to find trade signals, and using reverse-operation trading strategies to verify whether it is a relatively low-risk buying and selling point. The results of this study show that by statistically significant differences in profitability performance, it proves that there exist relatively low risk buying and selling points in the financial trading market. There are three contributions to this study: 1).This study refutes both the Efficient-market Theory and the Random Walk Theory and there is the existence of relatively low-risk buying and selling points in the market. 2). Verify the financial physics of the trading market 3). Verify the applicability of the new indicator definition for the market profile.

Using Artificial Neural Networks and Market Profile Theory to Construct the Trading Analysis in Stock Market

WITHDRAWN: Applying market profile theory to forecast Taiwan index futures market

As smartphones have rapidly emerged as a ubiquitous and indispensable technology, there are increasing indications that user dependence on smartphones and associated behaviors could be considered a form of addiction. Intensive interaction with smartphones has a significant impact on user thought patterns, cognitive focus and memory. Prospective memory is the mechanism which allows us to remember to do something at some future time or to remember some future event. Prospective memory plays an important role in our daily lives, acting as a mental calendar which organizes awareness of future events and thus allows people to plan accordingly. Continued reduced performance of prospective memory may cause us brain degradation. Intensive smartphone use creates long-term distractions, and thus reduces prospective memory use. Using a Smartphone Addiction Inventory questionnaire, followed by dual-task and electroencephalography (EEG) experiments to measure prospective memory performance, we found that some correlation between smartphone addiction and deterioration of prospective memory.

/pdf/exploring-the-impact-of-smartphone-addiction-in-prospective-3yk9lka331.pdf

Exploring the Impact of Smartphone Addiction in Prospective Memory

In this study, a novel procedure of time series dynamic behaviors clustering is proposed to improve the accuracy of minimum -variance optimal hedge ratio (OHR) estimation for future hedging. The dynamic behaviors of market fluctuation are extracted by measurement of variances, covariance, price spread, and their first and second differences. The behaviors with similar patterns are clustered using a growing hierarchical self-organizing map (GHSOM). The observations for OHR estimation are collected based on the hierarchical cluster structure and processed by within-cluster resampling. The spots and futures of the Taiwan Weighted Index (TWI) are adopted to demonstrate that the futures hedge effectiveness can be significantly improved.

An-Pin Chen

Papers

Strategy of global asset allocation using extended classifier system

Using Artificial Neural Networks and Market Profile Theory to Construct the Trading Analysis in Stock Market

WITHDRAWN: Applying market profile theory to forecast Taiwan index futures market

Exploring the Impact of Smartphone Addiction in Prospective Memory

Futures hedging using clusters with dynamic behavior of market fluctuation