4,348 research outputs found
Kernel methods in machine learning
We review machine learning methods employing positive definite kernels. These
methods formulate learning and estimation problems in a reproducing kernel
Hilbert space (RKHS) of functions defined on the data domain, expanded in terms
of a kernel. Working in linear spaces of function has the benefit of
facilitating the construction and analysis of learning algorithms while at the
same time allowing large classes of functions. The latter include nonlinear
functions as well as functions defined on nonvectorial data. We cover a wide
range of methods, ranging from binary classifiers to sophisticated methods for
estimation with structured data.Comment: Published in at http://dx.doi.org/10.1214/009053607000000677 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
by Law Hon Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves 67-70).Chapter 1 --- Introduction --- p.1Chapter 2 --- Automated Chromosome Classification --- p.4Chapter 2.1 --- Procedures in Chromosome Classification --- p.6Chapter 2.2 --- Sample Preparation --- p.7Chapter 2.3 --- Low Level Processing and Measurement --- p.9Chapter 2.4 --- Feature Extraction --- p.11Chapter 2.5 --- Classification --- p.15Chapter 3 --- Inference of Markov Networks by Dynamic Programming --- p.17Chapter 3.1 --- Markov Networks --- p.18Chapter 3.2 --- String-to-String Correction --- p.19Chapter 3.3 --- String-to-Network Alignment --- p.21Chapter 3.4 --- Forced Landmarks in String-to-Network Alignment --- p.31Chapter 4 --- Landmark Finding in Markov Networks --- p.34Chapter 4.1 --- Landmark Finding without a priori Knowledge --- p.34Chapter 4.2 --- Chromosome Profile Processing --- p.37Chapter 4.3 --- Analysis of Chromosome Networks --- p.39Chapter 4.4 --- Classification Results --- p.45Chapter 5 --- Speech Recognition using Inferred Markov Networks --- p.48Chapter 5.1 --- Linear Predictive Analysis --- p.48Chapter 5.2 --- TIMIT Speech Database --- p.50Chapter 5.3 --- Feature Extraction --- p.51Chapter 5.4 --- Empirical Landmarks in Speech Networks --- p.52Chapter 5.5 --- Classification Results --- p.55Chapter 6 --- Conclusion --- p.57Chapter 6.1 --- Suggested Improvements --- p.57Chapter 6.2 --- Concluding remarks --- p.61Appendix A --- p.63Reference --- p.6
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