Data classification

Xem 1-20 trên 140 kết quả Data classification
  • Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    pdf382p hoa_can 26-01-2013 44 16   Download

  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: ANMM4CBR: a case-based reasoning method for gene expression data classification...

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  • 4.2.3 MTMF MTMF combines the best parts of the Linear Spectral Mixing model and the statistical Matched Filter model while avoiding the drawbacks of each parent method (Boardman, 1998). It is a useful Matched Filter method without knowing all the possible endmembers in a landscape especially in case of subtle, sub-pixel occurrences. Firstly, pixel spectra and endmember spectra require a minimum noise fraction (MNF) (Green et al., 1988, Boardman, 1993) transformation. MNF reduces and separates an image into its most dimensional and non-noisy components.

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  • Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

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  • We exhibit a counterexample to Elliott’s classification conjecture for simple, separable, and nuclear C∗ -algebras whose construction is elementary, and demonstrate the necessity of extremely fine invariants in distinguishing both approximate unitary equivalence classes of automorphisms of such algebras and isomorphism classes of the algebras themselves.

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  • We will refer to embeddings providing a guarantee akin to that of Lemma 1.1 as JL- embeddings. In the last few years, such embeddings have been used in solving a variety of problems. The idea is as follows. By providing a low-dimensional representation of the data, JL-embeddings speed up certain algorithms dramatically, in particular algorithms whose run-time depends exponentially in the dimension of the working space. (For a number of practical problems the best-known algorithms indeed have such behavior.

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  • This paper proposes a method by which 5WlH (who, when, where, what, why, how, and predicate) information is used to classify and navigate Japaneselanguage texts. 5WlH information, extracted from text data, has an access platform with three functions: episodic retrieval, multi-dimensional classification, and overall classification. In a six-month trial, the platform was used by 50 people to access 6400 newspaper articles. The three functions proved to be effective for office documentation work and the precision of extraction was approximately 82%. ...

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  • Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data. We rely on the intuition that the sentiment labels for parallel sentences should be similar and present a model that jointly learns improved monolingual sentiment classifiers for each language. ...

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  • The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.

    pdf9p hongphan_1 14-04-2013 22 3   Download

  • We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. ...

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  • The amount of labeled sentiment data in English is much larger than that in other languages. Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g. Chinese) using labeled data in the source language (e.g. English).

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  • Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-ofthe-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. ...

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  • We present an NLP system that classifies the assertion type of medical problems in clinical notes used for the Fourth i2b2/VA Challenge. Our classifier uses a variety of linguistic features, including lexical, syntactic, lexicosyntactic, and contextual features. To overcome an extremely unbalanced distribution of assertion types in the data set, we focused our efforts on adding features specifically to improve the performance of minority classes.

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  • Educators are interested in essay evaluation systems that include feedback about writing features that can facilitate the essay revision process. For instance, if the thesis statement of a student’s essay could be automatically identified, the student could then use this information to reflect on the thesis statement with regard to its quality, and its relationship to other discourse elements in the essay. Using a relatively small corpus of manually annotated data, we use Bayesian classification to identify thesis statements.

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  • This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, constructed with the use of machine learning, to monitor the performance of the rule-based system. The training data for the second system is generated with the use of the rule-based system, thus avoiding the need for manual tagging. The disagreement of the two systems acts as a signal for updating the rule-based system.

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  • We present a novel application of NLP and text mining to the analysis of financial documents. In particular, we describe an implemented prototype, Maytag, which combines information extraction and subject classification tools in an interactive exploratory framework. We present experimental results on their performance, as tailored to the financial domain, and some forward-looking extensions to the approach that enables users to specify classifications on the fly.

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  • Chapter 2: Data Mining includes about Overview of data mining, Association rules, Classification, Regression, Clustering, Other Data Mining problems, Applications of data mining.

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  • This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve performance through fusing training data from multiple domains. To achieve this, we propose two approaches of fusion, feature-level and classifier-level, to use training data from multiple domains simultaneously. Experimental studies show that multi-domain sentiment classification using the classifier-level approach performs much better than single domain classification (using the training data individually). ...

    pdf4p hongphan_1 15-04-2013 20 1   Download

  • The paper addresses the problem of automatic enrichment of a thesaurus by classifying new words into its classes. The proposed classification method makes use of both the distributional data about a new word and the strength of the semantic relatedness of its target class to other likely candidate classes.

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  • Data Mining: Classification and Prediction presents about Classification with decision trees; Artificial Neural Networks; Algorithm for decision tree induction; Attribute Selection Measure; Extracting Classification Rules from Trees .

    ppt69p cocacola_10 08-12-2015 13 3   Download


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