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

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Modern Applied Statistics With S

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Human Development Report, 1990.@@@Human Development Report, 1991.

How do we find what we are looking for? Even when the desired target is in the current field of view, we need to search because fundamental limits on visual processing make it impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over timescales ranging from milliseconds to years, and the relative value of the targets and distractors. Modern theories of visual search need to incorporate all five factors and specify how these factors combine to shape search behaviour. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially important search tasks, from security screening to medical image perception. How do we find what we are looking for? Wolfe and Horowitz discuss the five factors that guide attention in visual search.

Five factors that guide attention in visual search

Overt attention in natural scenes: Objects dominate features

Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

Swarm intelligence for self-organized clustering

............................................................................................................................... XIX 1  Introduction ....................................................................................................................... 1 2  Fundamentals .................................................................................................................... 5 2.1  Basic Definitions ....................................................................................................... 5 2.2  Concepts of Graph Theory Applied to Patterns ....................................................... 10 2.3  Overview of Knowledge Discovery ........................................................................ 15 3  Approaches to Cluster Analysis ..................................................................................... 21 3.1  Common Clustering Methods .................................................................................. 22 3.2  Structure of Natural Clusters ................................................................................... 26 3.3  Problems with Clustering Methods ......................................................................... 29 4  Methods of Projection ..................................................................................................... 33 4.1  Common Approaches .............................................................................................. 33 4.2  Emergent Self-Organizing Map (ESOM) ................................................................ 37 4.3  Types of Projection Methods ................................................................................... 40 5  Visualizing the Output Space ......................................................................................... 43 5.1  Examples .................................................................................................................. 43 5.2  Structure Preservation .............................................................................................. 45 5.3  Generating a Topographic Map from the Generalized U*-matrix .......................... 46 6  Quality Assessments of Visualizations .......................................................................... 55 6.1  Common Quality Measures (QMs) ......................................................................... 58 6.2  Types of Quality Measures for Assessing Structure Preservation ........................... 67 6.3  Introducing the Delaunay Classification Error (DCE) ............................................ 73 7  Behavior-based Systems in Data Science ...................................................................... 77 7.1  Artificial Behavior Based on DataBots ................................................................... 80 7.2  Swarm Intelligence for Unsupervised Machine Learning ....................................... 83 7.3  Missing Links: Emergence and Game Theory ........................................................ 87 8  Databionic Swarm (DBS) ............................................................................................... 91 8.1  Projection with Pswarm ........................................................................................... 91 8.2  Comparing Pswarm with a Previously Developed Approach ................................. 98 8.3  Clustering on a Generalized U*-Matrix ................................................................ 104 VIII Table of contents 9  Experimental Methodology .......................................................................................... 107 9.1  Data Sets ................................................................................................................ 107 9.2  Parameter Settings ................................................................................................. 111 9.3  Gene Ontology (GO) ............................................................................................. 113 10  Results on Pre-classified Data Sets .............................................................................. 117 10.1  Comparison with Given Classifications ................................................................ 117 10.2  Evaluation of Projections Using the Delaunay Classification Error (DCE) .......... 120 10.3  Topographic Maps with Hypsometric Colors ........................................................ 122 11  DBS on Natural Data Sets ............................................................................................ 129 11.1  Types of Leukemia ................................................................................................ 129 11.2  World Gross Domestic Product (World GDP) ...................................................... 129 11.3  Tetragonula Bees ................................................................................................... 132 12  Knowledge Discovery with DBS .................................................................................. 137 12.1  Hydrology .............................................................................................................. 137 12.2  Pain Genes ............................................................................................................. 143 13  Discussion ....................................................................................................................... 149 14  Conclusion ...................................................................................................................... 161 References ............................................................................................................................. 163 Appendices ............................................................................................................................ 179 Supplement A:   Evaluation of Common QMs .................................................. 179 Supplement B:  Wine Dataset Distance Distribution ........................................ 185 Supplement C:  Generalized Umatrix of Pswarm and SOP .............................. 186 Supplement D:  DBS Visualizations of S-shape and uniform Cuboid ............. 191 Supplement E:  U-Matrix Visualizations of ESOM Projections ...................... 192 Supplement F:  Statistical Tests in Hydrology ................................................. 194 Supplement G:  3D Prints of Generalized Umatrix Visualizations of DBS ..... 195 Supplement H:  Contingency Table for Tetragonula Bees Clustering .............. 196 Supplement I:  Statistical Tests for FCPS clustering compared to DBS ......... 197 Index ...................................................................................................................................... 199

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.

https://www.mdpi.com/1422-0067/16/10/25897/pdf

Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.

/pdf/analyzing-the-fine-structure-of-distributions-5btpqzuj0y.pdf

Michael C. Thrun

Papers

Overt attention in natural scenes: Objects dominate features

Swarm intelligence for self-organized clustering

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)

Analyzing the fine structure of distributions.