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

In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification tasks mean that two or more attributes together provide stronger evidence for a particular class label. Knowledge of such interactions makes models more interpretable by revealing associations between attributes. This has applications, e.g., in pharmacovigilance to identify interactions between drugs or in bioinformatics to investigate associations between single nucleotide polymorphisms. We also show how the found attribute partitioning is related to a factorisation of the data generating distribution and empirically demonstrate the utility of the proposed method.

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Interpreting Classifiers through Attribute Interactions in Datasets.

We introduce a new search strategy, in which the information retrieval (IR) query is inferred from eye movements measured when the user is reading text during an IR task. In training phase, we know the users' interest, that is, the relevance of training documents. We learn a predictor that produces a "query" given the eye movements; the target of learning is an "optimal" query that is computed based on the known relevance of the training documents. Assuming the predictor is universal with respect to the users' interests, it can also be applied to infer the implicit query when we have no prior knowledge of the users' interests. The result of an empirical study is that it is possible to learn the implicit query from a small set of read documents, such that relevance predictions for a large set of unseen documents are ranked significantly better than by random guessing.

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Information Retrieval by Inferring Implicit Queries from Eye Movements

STUDY OBJECTIVES Examine the use of spectral heart rate variability (HRV) metrics in measuring sleepiness under chronic partial sleep restriction, and identify underlying relationships between HRV, Karolinska Sleepiness Scale ratings (KSS), and performance on the Psychomotor Vigilance Task (PVT). DESIGN Controlled laboratory study. SETTING Experimental laboratory of the Brain Work Research Centre of the Finnish Institute of Occupational Health, Helsinki, Finland. PARTICIPANTS Twenty-three healthy young males (mean age ± SD = 23.77 ± 2.29). INTERVENTIONS A sleep restriction group (N = 15) was subjected to chronic partial sleep restriction with 4 h sleep for 5 nights. A control group (N = 8) had 8 h sleep on all nights. MEASUREMENTS AND RESULTS Based on a search over all HRV frequency bands in the range [0.00, 0.40] Hz, the band [0.01, 0.08] Hz showed the highest correlation for HRV-PVT (0.60, 95% confidence interval [0.49, 0.69]) and HRV-KSS (0.33, 95% confidence interval [0.16, 0.46]) for the sleep restriction group; no correlation was found for the control group. We studied the fraction of variance in PVT explained by HRV and a 3-component alertness model, containing circadian and homeostatic processes coupled with sleep inertia, respectively. HRV alone explained 33% of PVT variance. CONCLUSIONS The findings suggest that HRV spectral power reflects vigilant attention in subjects exposed to partial chronic sleep restriction. CITATION Henelius A, Sallinen M, Huotilainen M, Muller K, Virkkala J, Puolamaki K. Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

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Heart Rate Variability for Evaluating Vigilant Attention in Partial Chronic Sleep Restriction

The label switching problem, the unidentifiability of the permutation of clusters or more generally latent variables, makes interpretation of results computed with MCMC sampling difficult. We introduce a fully Bayesian treatment of the permutations which performs better than alternatives. The method can even be used to compute summaries of the posterior samples for nonparametric Bayesian methods, for which no good solutions exist so far. Although being approximative in that case, the results are very promising. The summaries are intuitively appealing: A summarized cluster is defined as a set of points for which the likelihood of being in the same cluster is maximized.

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Bayesian Solutions to the Label Switching Problem

http://research.ics.aalto.fi/publications/kaip/10.1016_j.ipl.2008.12.003.pdf

Kai Puolamäki

Papers

Interpreting Classifiers through Attribute Interactions in Datasets.

Information Retrieval by Inferring Implicit Queries from Eye Movements

Heart Rate Variability for Evaluating Vigilant Attention in Partial Chronic Sleep Restriction

Bayesian Solutions to the Label Switching Problem

A randomized approximation algorithm for computing bucket orders