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

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

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Longman Student Grammar of Spoken and Written English March 13th, 2019 These tell us what choices are available in the grammar but we also need to understand how these choices are used to create discourse in different situations The year 1999 saw the publication of a large scale grammar of English with the aim of meeting the above needs the Longman ielts house net, longman student grammar of spoken and written english, longman grammar of spoken and written english roffel, longman student grammar of spoken and written english pdf, longman grammar of spoken and written english libros, longmans student grammar of spoken and written english, english longman grammar of spoken and written eng free, longman student grammar of spoken and written english, longman grammar of spoken and written english pdf web, lms2 vu edu pk, longman student grammar of spoken and written english, longman grammar of spoken and written english wikipedia, longman student grammar of spoken and written english, download pdf longman grammar of spoken and written, longman student grammar of spoken and written english, longman grammar of spoken and written english amazon co, longman student grammar of spoken and written english, longman grammar of spoken and written english edoc pub, the languagelab library longman student grammar of, longman grammar of spoken and written english scribd, longman grammar of spoken and written english free, the longman grammar of spoken and written english, longman grammar of spoken and written english epdf tips, grammars of spoken english new outcomes of corpus, longman grammar of spoken and written english tesl ej, book reviews longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english worldcat org, douglas biber et al longman grammar of spoken and, project muse longman grammar of spoken and written, longman grammar of spoken and written english oxford, 9780582237261 longman student grammar of spoken and, longman student grammar of spoken and written english, pdf longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english google books, student grammar of spoken and written english workbook, longman grammar of spoken and written english goodreads, longman student grammar of spoken and written english, longman student grammar of spoken and written english le, longman student grammar of spoken and written english, longman grammar of spoken and written english co construction, longman student grammar of spoken and written english, longman student grammar of spoken and written english by, longman student grammar of spoken and written english workbook, longman grammar of spoken and written english douglas

「会話の文法」に関する一考察 : Longman Grammar of Spoken and Written Englishの場合

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.

Statistically Significant Detection of Linguistic Change

Finding out whether a word occurs significantly more often in one text or corpus than in another is an important question in analysing corpora. As noted by Kilgarriff (Language is never, ever, ever, random, Corpus Linguistics and Linguistic Theory , 2005; 1(2): 263–76.), the use of the χ2 and log-likelihood ratio tests is problematic in this context, as they are based on the assumption that all samples are statistically independent of each other. However, words within a text are not independent. As pointed out in Kilgarriff (Comparing corpora, International Journal of Corpus Linguistics , 2001; 6(1): 1–37) and Paquot and Bestgen (Distinctive words in academic writing: a comparison of three statistical tests for keyword extraction. In Jucker, A., Schreier, D., and Hundt, M. (eds), Corpora: Pragmatics and Discourse . Amsterdam: Rodopi, 2009, pp. 247–69), it is possible to represent the data differently and employ other tests, such that we assume independence at the level of texts rather than individual words. This allows us to account for the distribution of words within a corpus. In this article we compare the significance estimates of various statistical tests in a controlled resampling experiment and in a practical setting, studying differences between texts produced by male and female fiction writers in the British National Corpus. We find that the choice of the test, and hence data representation, matters. We conclude that significance testing can be used to find consequential differences between corpora, but that assuming independence between all words may lead to overestimating the significance of the observed differences, especially for poorly dispersed words. We recommend the use of the t-test, Wilcoxon rank-sum test, or bootstrap test for comparing word frequencies across corpora.

/pdf/significance-testing-of-word-frequencies-in-corpora-57lp100jl8.pdf

Significance testing of word frequencies in corpora

Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people's prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network's connectivity. Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these. For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures. Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk.

https://dl.acm.org/doi/pdf/10.1145/3219819.3220074

Quantifying and Minimizing Risk of Conflict in Social Networks

Hypothesis testing using constrained null models can be used to compute the significance of data mining results given what is already known about the data. We study the novel problem of finding the smallest set of patterns that explains most about the data in terms of a global p value. The resulting set of patterns, such as frequent patterns or clusterings, is the smallest set that statistically explains the data. We show that the newly formulated problem is, in its general form, NP-hard and there exists no efficient algorithm with finite approximation ratio. However, we show that in a special case a solution can be computed efficiently with a provable approximation ratio. We find that a greedy algorithm gives good results on real data and that, using our approach, we can formulate and solve many known data-mining tasks. We demonstrate our method on several data mining tasks. We conclude that our framework is able to identify in various settings a small set of patterns that statistically explains the data and to formulate data mining problems in the terms of statistical significance.

A statistical significance testing approach to mining the most informative set of patterns

(i) Determine the sizes s1−n of each of the n corpus parts, which are normalized against the overall corpus size and correspond to expected percentages which take differently-sized corpus parts into consideration. (ii) Determine the frequencies v1−n with which a occurs in the n corpus parts, which are normalized against the overall number of occurrences of a and correspond to observed percentages. (iii) Compute all n pairwise absolute differences of observed and expected percentages, sum them up, and divide the result by two.

/pdf/correction-to-stefan-th-gries-dispersions-and-adjusted-2g04gbdity.pdf

Correction to Stefan Th. Gries’ “Dispersions and adjusted frequencies in corpora”, International Journal of Corpus Linguistics

Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or classification (if 'similar' means 'being more likely to have the same label'). In recent years various methods for NE have been introduced, all following a similar strategy: defining a notion of similarity between nodes (typically some distance measure within the network), a distance measure in the embedding space, and a loss function that penalizes large distances for similar nodes and small distances for dissimilar nodes.

A difficulty faced by existing methods is that certain networks are fundamentally hard to embed due to their structural properties: (approximate) multipartiteness, certain degree distributions, assortativity, etc. To overcome this, we introduce a conceptual innovation to the NE literature and propose to create \emph{Conditional Network Embeddings} (CNEs); embeddings that maximally add information with respect to given structural properties (e.g. node degrees, block densities, etc.). We use a simple Bayesian approach to achieve this, and propose a block stochastic gradient descent algorithm for fitting it efficiently.

We demonstrate that CNEs are superior for link prediction and multi-label classification when compared to state-of-the-art methods, and this without adding significant mathematical or computational complexity. Finally, we illustrate the potential of CNE for network visualization.

/pdf/conditional-network-embeddings-1xr0stl013.pdf

Jefrey Lijffijt

Papers

Significance testing of word frequencies in corpora

Quantifying and Minimizing Risk of Conflict in Social Networks

A statistical significance testing approach to mining the most informative set of patterns

Correction to Stefan Th. Gries’ “Dispersions and adjusted frequencies in corpora”, International Journal of Corpus Linguistics

Conditional Network Embeddings.