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

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Statistics for Spatial Data.

Objectives: Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with : Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). Methods: A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) curve analyses and to defi ne an optimum cut-off score for the LJQ. curve analyses and to defi ne an optimum cut-off score for the LJQ. Results: The 19-item LJQ had satisfactory internal : The 19-item LJQ had satisfactory internal consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. Conclusions: Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. KEYWORDS Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern

Development and Validation of A

Zircon is the main mineral in the majority of igneous and metamorphic rocks with Zr as an essential structural constituent. It is a host for significant fractions of the whole-rock abundance of U, Th, Hf, and the REE (Sawka 1988, Bea 1996, O’Hara et al. 2001). These elements are important geochemically as process indicators or parent isotopes for age determination. The importance of zircon in crustal evolution studies is underscored by its predominant use in U-Th-Pb geochronology and investigations of the temporal evolution of both the crust and lithospheric mantle. In the past decade an increasing interest in the composition of zircon, trace-elements in particular, has been motivated by the effort to better constrain in situ microprobe-acquired isotopic ages. Electron-beam compositional imaging and isotope-ratio measurement by in situ beam techniques—and the micrometer-scale spatial resolution that is possible—has revealed in many cases that single zircon crystals contain a record of multiple geologic events. Such events can either be zircon-consuming, alteration, or zircon-forming and may be separated in time by millions or billions of years. In many cases, calculated zircon isotopic ages do not coincide with ages of geologic events determined from other minerals or from whole-rock analysis. To interpret the geologic validity and significance of multiple ages, and ages unsupported by independent analysis of other isotopic systems, has been the impetus for most past investigations of zircon composition. Some recent compositional investigations of zircon have not been directly related to geochronology, but to the ability of zircon to influence or record petrogenetic processes in igneous and metamorphic systems.

Sedimentary rocks may also contain a significant fraction of zircon. Although authigenic zircon has been reported (Saxena 1966, Baruah et al. 1995, Hower et al. 1999), it appears to be very rare and may in fact be related to …

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The Composition of Zircon and Igneous and Metamorphic Petrogenesis

Preface 1. The purpose of the book 2. Survey of contents 3. How to use the book 4. Notation, terminology and conventions 5. Acknowledgements Part I. Introduction: Part II. Descriptive Methods: 2.1. Introduction 2.2. Data display 2.3. Simple summary quantities 2.4. Modifications for axial data Part III. Models: 3.1. Introduction 3.2. Notation trigonometric moments 3.3. Probability distributions on the circle Part IV. Analysis of a Single Sample of Data: 4.1. Introduction 4.2. Exploratory analysis 4.3. Testing a sample of unit vectors for uniformity 4.4. Nonparametric methods for unimodal data 4.5. Statistical analysis of a random sample of unit vectors from a von Mises distribution 4.6. Statistical analysis of a random sample of unit vectors from a multimodal distribution 4.7. Other topics Part V. Analysis of Two or More Samples, and of Other Experimental Layouts: 5.1. Introduction 5.2. Exploratory analysis 5.3. Nonparametric methods for analysing two or more samples of unimodal data 5.4. Analysis of two or more samples from von Mises distributions 5.5. Analysis of data from more complicated experimental designs Part VI. Correlation and Regression: 6.1. Introduction 6.2. Linear-circular association and circular-linear association 6.3. Circular-circular association 6.4. Regression models for a circular response variable Part VII. Analysis of Data with Temporal or Spatial Structure: 7.1. Introduction 7.2. Analysis of temporal data 7.3. Spatial analysis Part VIII. Some Modern Statistical Techniques for Testing and Estimation: 8.1. Introduction 8.2. Bootstrap methods for confidence intervals and hypothesis tests: general description 8.3. Bootstrap methods for circular data: confidence regions for the mean direction 8.4. Bootstrap methods for circular data: hypothesis tests for mean directions 8.5. Randomisation, or permutation, tests Appendix A. Tables Appendix B. Data sets References Index.

Statistical Analysis of Circular Data

Trace element abundances in igneous zircons, as determined by electron microprobe and laser-ablation microprobe ICPMS analysis, are shown to be sensitive to source rock type and crystallisation environment. The concentrations of 26 trace elements have been determined for zircons from a wide range of different rock types and reveal distinctive elemental abundances and chondrite-normalised trace element patterns for specific rock types. There is a general trend of increasing trace element abundance in zircons from ultramafic through mafic to granitic rocks. The average content of REE is typically less than 50 ppm in kimberlitic zircons, up to 600–700 ppm in carbonatitic and lamproitic zircons and 2,000 ppm in zircons from mafic rocks, and can reach per cent levels in zircons from granitoids and pegmatites. Relatively flat chondrite-normalised REE patterns with chondrite-normalised Yb/Sm ratios from 3 to 30 characterise zircons from kimberlites and carbonatites, but Yb/Sm is commonly over 100 in zircons from pegmatites. Th/U ratios typically range from 0.1 to 1, but can be 100–1000 in zircons from some carbonatites and nepheline syenite pegmatites. The geochemical signatures characteristic of zircon from some rock types can be recognised in bivariate discriminant diagrams, but multivariate statistical analysis is essential for the discrimination of zircons from most rock types. Classification trees based on recursive partitioning techniques provide a rapid means of relating parent rock type to zircon trace element analysis; zircons from many rock types can be discriminated at confidence levels of 75% or more. These trees allow recognition of the provenance of detrital zircons from heavy mineral concentrates, and significantly enhance the usefulness of zircon in regional crustal studies and as an indicator mineral in mineral exploration.

Igneous zircon: trace element composition as an indicator of source rock type

This is the first comprehensive, yet clearly presented, account of statistical methods for analysing spherical data. The analysis of data, in the form of directions in space or of positions of points on a spherical surface, is required in many contexts in the earth sciences, astrophysics and other fields, yet the methodology required is disseminated throughout the literature. Statistical Analysis of Spherical Data aims to present a unified and up-to-date account of these methods for practical use. The emphasis is on applications rather than theory, with the statistical methods being illustrated throughout the book by data examples.

Statistical Analysis of Spherical Data

Statistical Analysis of Spherical Data.

Many data analytic questions can be formulated as (noisy) optimization problems. They explicitly or implicitly involve finding simultaneous combinations of values for a set of (’’input‘‘) variables that imply unusually large (or small) values of another designated (’’output‘‘) variable. Specifically, one seeks a set of subregions of the input variable space within which the value of the output variable is considerably larger (or smaller) than its average value over the entire input domain. In addition it is usually desired that these regions be describable in an interpretable form involving simple statements (’’rules‘‘) concerning the input values. This paper presents a procedure directed towards this goal based on the notion of ’’patient‘‘ rule induction. This patient strategy is contrasted with the greedy ones used by most rule induction methods, and semi-greedy ones used by some partitioning tree techniques such as CART. Applications involving scientific and commercial data bases are presented.

Nicholas I. Fisher

Papers

Statistical Analysis of Circular Data

Igneous zircon: trace element composition as an indicator of source rock type

Statistical Analysis of Spherical Data

Statistical Analysis of Spherical Data.

Bump hunting in high-dimensional data