Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Canonical analysis is an ordination technique which allows a direct comparison of two data matrices. It is often applied in ecological studies and here it is shown how canonical analysis, in particular redundancy analysis, can be used to analyze sensory evaluation data. The example data are obtained with individual vocabulary profiling study of concert hall acoustics. The redundancy analysis is compared with hierarchical multiple factor analysis. In addition, it is shown how redundancy analysis can be used to explain the subjective data with different objective data, in this case room acoustical parameters and physical measures of the studied concert halls.

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Canonical analysis of individual vocabulary profiling data

Eyetools was born in 2000 out of the Stanford University Advanced Eye Interpretation Project. After seeing the business value of eyetracking resulting from the Stanford-Poynter Project, a collaborative study between the Poynter Institute and Stanford University’s Department of Communications around the viewing of online news sites, founder Greg Edwards spun out Eyetools. Since then, Eyetools’ pioneering work in inferring mental state from eye movements and visualizing eyetracking data has led to several key patents in the area, and has enabled eyetracking to be put into use more easily by an ever expanding number of companies and people. Eyetools’ roots in Human-Computer Interaction began in 1995 ∗PASCAL Invited Talk

Proceedings of the NIPS 2005 Workshop on Machine Learning for Implicit Feedback and User Modeling

Aim: (1) Survey and organize computational approaches to fossil data analysis into a methodological framework. (2) Highlight the kinds of research questions about evolutionary and environmental change that can be answered by applying computational algorithms to mammal fossil data to better understand past ecosystems and climates. Questions: What models have been used for what research questions? What is their scope of application? What are their potential limitations? Search methods: Our search of the literature was based on personal knowledge in combination with keyword-based searches. Papers were considered relevant if data-driven computational methods were used to analyse relationships between organisms and their environments at evolutionary time scales. Conclusions: We demonstrate that different research questions may be answered with the same computational algorithm, and different algorithms may be needed to answer the same question in different contexts. We believe that in order to move forward, we need to match knowledge of methods with knowledge of the fossil record in a research question-driven way. Figure 2 presents a proposed workflow. Following this framework, we survey existing work and highlight what research questions can potentially be answered with which methods, some of which may not have been reported in the evolutionary palaeontology literature to date. The outcome of this survey is a proposal for a research agenda in computational fossil data analysis.

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A survey of computational methods for fossil data analysis

Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, which are then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and is robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We provide an open-source implementation of the framework.

Guided Visual Exploration of Relations in Data Sets.

Understanding relations in datasets is important for the successful application of data mining and machine learning methods. This paper describes tiler, a software tool for interactive visual explorative data analysis realising the interactive Human-Guided Data Exploration framework. tiler allows a user to formulate different hypotheses concerning the relations in a dataset. Data samples corresponding to these hypotheses are then compared visually, allowing the user to gain insight into relations in the dataset. The exploration process is iterative and the user gradually builds up his or her understanding of the data. Code related to this paper is available at: https://github.com/aheneliu/tiler.

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Kai Puolamäki

Papers

Canonical analysis of individual vocabulary profiling data

Proceedings of the NIPS 2005 Workshop on Machine Learning for Implicit Feedback and User Modeling

A survey of computational methods for fossil data analysis

Guided Visual Exploration of Relations in Data Sets.

Tiler: Software for Human-Guided Data Exploration.