Theory of Point Estimation(点估计理论)
经典著作,搞统计和机器学习的同志会用到它的。This book is concerned with point estimation in Euclidean sample spaces.
The first four chapters deal with exact (small-sample) theory, and their approach
and organization parallel those of the companion volume, Testing Statistical
Hypotheses (TSH). Optimal estimators are derived according to criteria such as
unbiasedness, equivariance, and minimaxity, and the material is organized
around these criteria. The principal applications are to exponential and group
families, and the systematic discussion of the rich body of (relatively simple)
statistical problems that fall under these headings constitutes a second major
theme of the book.
Modern Multidimensional Scaling, Theory and Applications, 2ed (Springer Series in Statistics) (Ingwer Borg, Patrick J. F. Groenen)
Multidimensional scaling (MDS) is a technique for the analysis of similarity
or dissimilarity data on a set of objects. Such data may be intercorrelations
of test items, ratings of similarity on political candidates, or trade indices
for a set of countries. MDS attempts to model such data as distances among
points in a geometric space. The main reason for doing this is that one wants
a graphical display of the structure of the data, one that is much easier to
understand than an array of numbers and, moreover, one that displays the
essential information in the data, smoothing out noise.
There are numerous varieties of MDS. Some facets for distinguishing
among them are the particular type of geometry into which one wants to
map the data, the mapping function, the algorithms used to find an optimal
data representation, the treatment of statistical error in the models, or the
possibility to represent not just one but several similarity matrices at the
same time. Other facets relate to the different purposes for which MDS
has been used, to various ways of looking at or “interpreting” an MDS
representation, or to differences in the data required for the particular
models.
In this book, we give a fairly comprehensive presentation of MDS. For the
reader with applied interests only, the first six chapters of Part I should
be sufficient. They explain the basic notions of ordinary MDS, with an
emphasis on how MDS can be helpful in answering substantive questions.
Later parts deal with various special models in a more mathematical way
and with particular issues that are important in particular applications of
MDS. Finally, the appendix on major MDS computer programs helps the
reader to choose a program and to run a job.
Pattern Recognition And Machine Learning
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propagation.
Similarly, new models based on kernels have had significant impact on both
algorithms and applications.
This new textbook reflects these recent developments while providing a comprehensive
introduction to the fields of pattern recognition and machine learning. It is
aimed at advanced undergraduates or first year PhD students, as well as researchers
and practitioners, and assumes no previous knowledge of pattern recognition or machine
learning concepts. Knowledge of multivariate calculus and basic linear algebra
is required, and some familiarity with probabilities would be helpful though not essential
as the book includes a self-contained introduction to basic probability theory.
Decision Tree Primer
Decision Tree Primer。很好的DT学习资料,原理讲得很清楚。值得一看。
蜜罐与蜜网技术(CNCERT技术培训)
蜜罐与蜜网技术(CNCERT技术培训). Honeynet的培训资料。
C#版本的SVM实现代码
C#版本的SVM实现代码。实现了基本的SVM算法。