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Linear Versus Nonlinear Classifiers - Stanford University

Web In two dimensions, a linear classifier is a line. Five examples are shown in Figure 14.8.These lines have the functional form .The classification rule of a linear classifier is to assign a document to if and to if .Here, is the two-dimensional vector representation of the document and is the parameter vector that defines (together with ) the decision boundary.

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The Stanford Natural Language Processing Group

(53 years ago) Web The Stanford NLP Group Welcome to the Natural Language Processing Group at Stanford University! We are a passionate, inclusive group of students and faculty, postdocs and research engineers, who work together on algorithms that allow computers to process, generate, and understand human languages.

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The Stanford Natural Language Processing Group

(53 years ago) Web About | Questions | Mailing lists | Download | Extensions | Release history | FAQ. About. A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS …

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Support vector machines: The linearly separable case

(53 years ago) Web Again, the points closest to the separating hyperplane are support vectors. The geometric margin of the classifier is the maximum width of the band that can be drawn separating the support vectors of the two classes. That is, it is twice the minimum value over data points for given in Equation 168, or, equivalently, the maximal width of one of the fat separators …

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Introduction to Information Retrieval - Stanford University

(53 years ago) Web Introduction to Information Retrieval. This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.. You can order this book at CUP, at your local bookstore or on the internet.The best search term to use is the ISBN: …

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Software - The Stanford Natural Language Processing Group

(53 years ago) Web The Stanford NLP Group makes some of our Natural Language Processing software available to everyone! We provide statistical NLP, deep learning NLP, and rule-based NLP tools for major computational linguistics problems, which can be incorporated into applications with human language technology needs.

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Stanford TACRED Homepage

(53 years ago) Web Introduction. TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled …

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Single-link and complete-link clustering - Stanford University

(53 years ago) Web Figure 17.4 depicts a single-link and a complete-link clustering of eight documents. The first four steps, each producing a cluster consisting of a pair of two documents, are identical. Then single-link clustering joins the upper two pairs (and after that the lower two pairs) because on the maximum-similarity definition of cluster similarity, those two clusters are …

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Contents

(53 years ago) Web © 2008 Cambridge University Press This is an automatically generated page. In case of formatting errors you may want to look at the PDF edition of the book. 2009-04-07

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GloVe: Global Vectors for Word Representation - Stanford University

(53 years ago) Web GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

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Evaluation of clustering - Stanford University

(53 years ago) Web K-means Up: Flat clustering Previous: Cardinality - the number Contents Index Evaluation of clustering Typical objective functions in clustering formalize the goal of attaining high intra-cluster similarity (documents within a cluster are similar) and low inter-cluster similarity (documents from different clusters are dissimilar).

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An Introduction to Logistic Regression - Stanford University

(53 years ago) Web An Introduction to Logistic Regression JohnWhitehead Department of Economics East Carolina University Outline Introduction and Description Some Potential Problems and Solutions Writing Up the Results Introduction and Description Why use logistic regression?

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Single-Link, Complete-Link & Average-Link Clustering

(53 years ago) Web There is now an updated and expanded version of this page in form of a book chapter. Single-Link, Complete-Link & Average-Link Clustering. Hierarchical clustering treats each data point as a singleton cluster, and then successively merges clusters until all points have been merged into a single remaining cluster.

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Introduction to Information Retrieval - Stanford University

(53 years ago) Web Introduction to Information Retrieval. This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.. You can order this book at CUP, at your local bookstore or on the internet.The best search term to use is the ISBN: …

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Faster postings list intersection via skip pointers - Stanford …

(53 years ago) Web Consider first efficient merging, with Figure 2.9 as an example. Suppose we've stepped through the lists in the figure until we have matched on each list and moved it to the results list. We advance both pointers, giving us on the upper list and on the lower list. The smallest item is then the element on the top list. Rather than simply advancing the upper pointer, …

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