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

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

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The Graph Neural Network Model

We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by combining domain knowledge with statistical data.

A Tutorial on Learning Bayesian Networks

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.

Deep learning in bioinformatics

Statistics: Methods and Applications.

Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing with this uncertainty using an underlying graphical structure and the probability calculus. Bayesian networks have been successfully implemented in areas as diverse as medical diagnosis and finance. We present a brief introduction to Bayesian networks for those readers new to them and give some pointers to the literature.

Introduction to Bayesian Networks

Innovations in Bayesian network , Innovations in Bayesian network , کتابخانه دیجیتال جندی شاپور اهواز

Innovations in Bayesian Networks

Multilayer perceptrons (MLPs) and support vector machines (SVMs) are flexible machine learning techniques that can fit complex nonlinear mappings. MLPs are the most popular neural network type, consisting on a feedforward network of processing neurons that are grouped into layers and connected by weighted links. On the other hand, SVM transforms the input variables into a high dimensional feature space and then finds the best hyperplane that models the data in the feature space. Both MLP and SVM are gaining an increase attention within the data mining (DM) field and are particularly useful when more simpler DM models fail to provide satisfactory predictive models. This tutorial chapter describes basic MLP and SVM concepts, under the CRISP-DM methodology, and shows how such learning tools can be applied to real-world classification and regression DM applications.

Data Mining: Foundations and Intelligent Paradigms

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.

Dawn E. Holmes

Papers

Introduction to Bayesian Networks

Innovations in Bayesian Networks

Data Mining: Foundations and Intelligent Paradigms

Innovations in Bayesian Networks: Theory and Applications

Innovations in Machine Learning