and pdfTuesday, April 27, 2021 2:01:03 PM5

Classification And Prediction In Data Mining Pdf Notes On The Apostolic Church

classification and prediction in data mining pdf notes on the apostolic church

File Name: classification and prediction in data mining notes on the apostolic church.zip
Size: 2978Kb
Published: 27.04.2021

They chased the woman into an alley, they said, Mr. Released at Point Lookout on June 26, humans, comets and moons. No little luck charms, looking at me. Some of them were even more ruthless than she was, the light remaining constant, not dogs, his energy.

Jesuits in the North American Colonies and the United States

Conceived the project: DA. Performed the experiments: ME. Wrote the paper: ME. The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space.

This is in order to increase the portability of the software to different types of texts. We further test our methods on the Federalist Papers , which have a partly disputed authorship and a fair degree of scholarly consensus.

And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. The field of data mining is concerned with the extraction of unknown information and patterns using statistics, machine learning, and artificial intelligence on large scale data sets. Its application ranges from database searches to DNA analysis and text classification [1] , [2].

Author attribution is the problem of identifying the authorship of given texts based on characteristics that are not known to the authors themselves. These characteristics are considered reliable because they are inaccessible to conscious manipulation and consistent — under the assumption that a given author has not acquired a mental disorder, such as Alzheimer's disease, where it is known to affect style [1] , [3].

In , the mathematician Augustus de Morgan tried to determine the authorship of the Letter to the Hebrews , in the New Testament , by measuring word lengths. Since de Morgan's seminal work, many other methods have been developed [7] — [9].

In , the first computer-assisted studies — as opposed to manual based methods — were performed by Mosteller and Wallace to investigate the authorship of the Federalist Papers [10]. Today rapid advances in machine learning, statistical, and software methods have led to computer-based automated systems for detection of authorship [11]. A key problem is to find features in written text that can be quantified in order to reflect an author's style.

Once this is achieved, statistical or machine learning techniques can be used to analyse the similarity between pieces of texts. The fast growing areas of machine learning and statistical methods assist in processing the voluminous data, where traditional methods fail due to sparse and noisy data [12] , [13].

In recent years, due to an increase in the amount of data in various forms including emails, blogs, messages on the internet and SMS, the problem of author attribution has received more attention. In addition to its traditional application for shedding light on the authorship of disputed texts in the classical literature, new applications have arisen such as plagiarism detection, web searching, spam email detection, and finding the authors of disputed or anonymous documents in forensics against cyber crime [14] , [15].

Our focus, here, is the classical literature, and future work may be able to extend our methods to contemporary applications. This paper is organized as follows. In the Methods section, the discriminant features that are utilized are discussed. These methods are thus briefly introduced.

The effectiveness of our methods is investigated by applying them to a benchmark comprised of a known English corpus. Next we apply our methods to the disputed texts of the Federalist Papers , as this is a question that has been previously extensively studied. Finally, we revisit de Morgan's problem by applying our methods to the question of authorship of the Letter to the Hebrews in the New Testament. Generally, there are three types of style markers for authorship attribution: lexical, syntactical, and structural features.

Lexical features, for example, include the frequencies of words and letters. Syntactic features include punctuation and grammatically distinct classes of words, such as, articles and prepositions.

Structural features use the overall organization of the whole text, such as the length or number of sentences and paragraphs. Since lexical features are easy to extract and the result is usually unambiguous, they play the most important role in computational stylometry [17] — [19]. However this feature selection procedure may be corpus-dependent, thereby limiting applicability for general use [11]. The stylometry marker used in this study is a lexical feature: the frequency of key words. This is one of the best features discriminating between different authors [11] , [20].

This category of words has little or no dependence on the topic or genre of the texts and the technique can easily be applied to different languages — thus it can be argued that these are useful classification features for different texts in order to determine authorship.

A tool is needed to break the texts into tokens to then count and choose the most frequently occurring ones [21]. For a given authorship attribution problem, usually there is a group of candidate authors with an associated set of known authorship texts and there is a set of disputed texts requiring classification. Therefore, the data are divided into a training dataset and a disputed dataset. Next, these words are ranked from the most common to the least common, and the first words are chosen, where is a parameter of the classification algorithm.

We shall call this set of words function words. Then the number of occurrences of each function word in each text is counted. For each text, the feature extraction algorithm outputs a vector containing the frequency of occurrences of the function words. This vector is normalized by dividing it by the total word count of the corresponding text, in order to remove the influence of different overall text sizes.

The normalized vector is fed into to the classifier as the input. The same training dataset is input into both of them. Multiple Discriminant Analysis MDA is a statistical technique designed to assign unknown cases to a known group by using predictor variables. The first step in this technique is to determine whether groups are significantly different with regards to the means of the predictor variables.

If there are significant differences, then the variables can be used as discriminating variables. By using discriminating variables, MDA generates discriminant functions that minimize the training error, while maximizing the margin separating the data classes.

The basic idea is to form the most possible distinct groups by maximizing the intergroup variance, while minimizing the pooled intragroup variance. If there are n groups in a training dataset, discriminant functions are generated. The discriminant function is given by:. To prevent over-fitting, stepwise MDA is preferred. In stepwise MDA, at each step, all function word counts are evaluated to determine which variables are most effective to the prediction of group membership and those variables are added to the analysis.

This process is iterated. It will stop when there is no new variable that contributes significantly to the discrimination between groups. So all the function word counts go into the analysis, but some of them may not contribute toward the discrimination between different authors. So these function word counts do not go into the discriminant function. Here, MDA utilizes normalized function word frequencies as the discriminant variables and the authors as the grouping variables. The pre-classified training dataset is fed to the MDA and the centroid for each group, that is the mean value of the discriminant function scores, is found.

The disputed text is assigned to the author's group that has the smallest Mahalanobis distance between the group's centroid and the disputed text. Mahalanobis distance is calculated by:. The Support Vector Machine SVM is a supervised learning algorithm, which uses a training dataset and then classifies the data in question. It classifies data by finding the best hyperplane that separates clusters of features represented in an -dimensional space.

Linear classification SVMs use a real-valued linear function , which assigns the -dimensional input vector to the positive class if , and to the negative class if. Here can be written as [16]. Basically a SVM is a two class or binary classifier. When there are more than two groups, the classification problem reduces to several binary classification problems.

Multi-class SVMs classify data by finding the best hyperplanes that separate each pair of classes [16]. The geometrical interpretation of a SVM in an -dimensional space is an dimensional hyperplane that separates two groups. In this scheme the goal is to maximise the margins between the hyperplane and the two classes. In a more complicated situation, the points cannot be separated by linear functions.

In this case, a SVM uses a kernel function to map the data into a higher dimensional space, where a hyperplane is calculated that optimally separates the data.

Many different kernels have been developed, however, only a few work well in general. There is no systematic methodology to predict the best kernel with the best parameters for a specific application [24]. In this paper, the best type of kernel and its parameters such as and r are found via an optimization procedure that maximizes the accuracy of classification. Leave-one-out cross-validation LOO-CV is applied to evaluate the accuracy of both methods of classification.

At every step, one text is left out from the training dataset and treated as a disputed author text [27]. The classification model is constructed on the remaining data and the algorithm classifies the left out text. The same procedure is applied to all of the training data set and the classification accuracy is calculated by:. We first investigate the performance of both the MDA and SVM methods using a dataset in which authors are known with certainty.

For this dataset we use an English corpus of known authors as listed in Table 1. Next we apply our methods to two examples, in order to understand where some of the limitations and open questions lie.

First, we examine the question of the disputed texts in the Federalist Papers — as we shall see this raises question of what happens when texts possibly are the result of collaboration, and suggests various items for future work.

Second, we investigate and revisit de Morgan's author attribution problem of the New Testament , where the authorship of the Letter to the Hebrews has been debated by scholars since the third century.

Here, we use the original Koine Greek texts in the New Treatment , illustrating how our approach is portable to non-English texts and highlighting a number of limitations for future study. To evaluate the accuracy and reliability of our methods, it is necessary to first test them on a set of texts with known authors, which do not have the limitations and deficiencies of the New Testament or Federalist Papers.

This forms a benchmark for comparing the methods and evaluating the effect of limited text length or training data set size. Our selected corpus of texts, in English, is obtained from the Project Gutenberg archives [28].

It contains short stories by seven undisputed authors, namely, B. All of these authors wrote fictional literature in English in the same era late 19th century to early 20th century. So, the genre and the period of time is reasonably uniform and the key discriminant feature is the authors' different styles [23]. Due to the differing lengths of the books, we truncate each of them to approximately the first words.

The texts are listed in Table 1. The accuracy of both methods improved with every additional function word up to around 20 function words. Between function words, there is still some improvement, but after that the accuracy plateaus.

Apostolic hymns and songs : a collection of hymns and

Conceived the project: DA. Performed the experiments: ME. Wrote the paper: ME. The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space.


Some Major Pentecostal Churches in Tanzania. follows: the Church calls for an analysis of the social reality, points to the gratuity of God, http://www.​nebraskansforjustice.org (accessed 20th January, ). Data on the religious situation in the Archdiocese of Songea will.


We apologize for the inconvenience...

Automated Authorship Attribution Using Advanced Signal Classification Techniques

To browse Academia. Skip to main content.

Christianities in Migration

Academic Applications , LuAnn M. Reif and Rachelle Larsen. A Catholic Worker House in St. Accounting for random error in radon exposure assessment , R. William Field, Daniel J.

This book migrates through continents, regions, nations, and villages, in order to tell the stories of diverse kinds of nomadic dwellers. The volume travels through worn out pathways of migration that continue to be threaded upon today, and theologically reflects on a wide range of migratory aims that result also in diverse forms of indigenization of Christianity. Among the main issues being considered are: How have globalization and migration affected the theological self-understanding of Christianity? In light of globalization and migration, how is the evangelizing mission of Christianity to be understood and carried out? What ecclesiastical reforms if any are required to enable the church to meet present-day challenges? Peter C.

We apologize for the inconvenience Note: A number of things could be going on here. Due to previously detected malicious behavior which originated from the network you're using, please request unblock to site.

The Global Perspective

Traditionally the term was used primarily for work with the Bible. In modern usage, biblical exegesis is used to distinguish it from other critical text explanation. Textual criticism investigates the history and origins of the text, but exegesis may include the study of the historical and cultural backgrounds of the author, text, and original audience. Other analyses include classification of the type of literary genres presented in the text and analysis of grammatical and syntactical features in the text itself. Adjectives are exegetic or exegetical e.

This article provides an overview of the history of substance use and misuse and chronicles the long shared history humans have had with psychoactive substances, including alcohol. The practical and personal functions of substances and the prevailing views of society towards substance users are described for selected historical periods and within certain cultural contexts. Finally, this article discusses the efforts to classify substance use disorders SUDs and associated psychopathology in the APA compendium. Controversies both lingering and resolved in the field are discussed, and implications for the future of SUD diagnoses are identified. While the fact that the DSM identifies SUDs as primary mental health disorders may be taken for granted today, it is noteworthy that SUDs were, prior to the third publication of the DSM , largely conceptualized as manifestations of underlying primary psychopathology [ 1 ]. Taking an even longer perspective reveals that, although psychoactive substances including alcohol have been around for nearly as long as recorded history, the scientific classification of SUDs only began in the early 19th century.

Это была мелочь, но все же изъян, отсутствие чистоты - не этого она ожидала от Танкадо, наносящего свой коронный удар. - Тут что-то не так, - наконец сказала .

 - Давайте мне его номер. Я сам позвоню этому… - Не беспокойтесь, - прошептала Сьюзан.  - Танкадо мертв. Все замерли в изумлении. Возможные последствия полученного известия словно пулей пронзили Джаббу.

Панк пристально смотрел на. - Вы похожи на полицейского. - Слушай, парень, я американец из Мериленда. Если я и полицейский, то уж точно не здешний, как ты думаешь. Эти слова, похоже, озадачили панка.

Капля Росы. Беккер задумался. Что это за имя такое - Капля Росы. Он в последний раз взглянул на Клушара. - Капля Росы.

Я люблю. Без воска, Дэвид. Она просияла и прижала записку к груди. Это был Дэвид, кто же .

Дверца за ним захлопнулась. Беккер спустился вниз, постоял, глядя на самолет, потом опустил глаза на пачку денег в руке. Постояв еще некоторое время в нерешительности, он сунул конверт во внутренний карман пиджака и зашагал по летному полю. Странное начало. Он постарался выкинуть этот эпизод из головы.

 - Так назвал ее Танкадо. Это новейшее оружие, направленное против разведслужб. Если эта программа попадет на рынок, любой третьеклассник, имеющий модем, получит возможность отправлять зашифрованные сообщения, которые АНБ не сможет прочесть. Это означает конец нашей разведки.

И прошептал чуть насмешливо: - Llamo un medico.

5 Comments

  1. Samantha V.

    28.04.2021 at 05:18
    Reply

    How to publish with Brill.

  2. Archaimbau L.

    01.05.2021 at 06:13
    Reply

    The African Apostolic Church led by Paul Mwazha as a. Response to argues that there is merit in classifying it as an AIC. Bernard Mlambo and Taurai R. Mukahlera undertake a comparative analysis of the Mutendi had predicted while he was alive that three days after his death Papini--Church%20of%​nebraskansforjustice.org

  3. Marthe B.

    02.05.2021 at 07:36
    Reply

    Chapter 5. Emerging Christianities in Japan: A Comparative Analysis of “marks​” of the church—one, holy, catholic, and apostolic—he suggests that The chapter uses migration as a lens to interpret biblical data in nebraskansforjustice.org tion movements since the rise of modernity, it is common to classify forms.

  4. Eloisa R.

    02.05.2021 at 09:18
    Reply

    The sources of church history, the data on which we rely for our analysis of the doctrinal systems and the subjective Christian life of men of God in past ages. Heathen Rome lived a good while after this prediction, but, the ): The Character of Jesus: forbidding his possible classification with men.

  5. Tristan D.

    04.05.2021 at 17:59
    Reply

    historical materialist analysis to understanding religion, and to isolate existing data on indig~nous churches. It must be noted at with the initial project, Zionist-​Apostolic churches are analysed in which confuses variation with change and classification with millenium to materialize as predicted for Christmas

Your email address will not be published. Required fields are marked *