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Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space.

本論文的目的，在探討我國自願性財務預測公告與證券信用交易之間的關係。信用交易的增減代表使用信用交易的投資者對某特定資訊的瞭解與使用，因此實證檢視財務預測的修正行為與信用交易增減的關係，可以敏銳地瞭解，是種特定投資者在哪個時點對財務預測修正進行理性預期，並予使用且做了較實際的交易行為。因此，本研究的測試可以瞭解使用信用交易的投資者如何使用財務預測等相關資訊。據此，本研究的結果有助於了解使用信用交易的投資者如何運用自願性財務預測資訊來做投資決策。 　　研究期問是以民國八十四年至八十六年的資料為分析的對象，研究的結果顯示： 　　一、在季報（半年報、年報）公告前公佈的財務預測，好消息會引起融資顯著增加，融券增加幅度雖不如融資大，但結果亦為顯著；壞消息會使融資及融券同樣顯著增加，但融資增加幅度亦較融券顯著。 　　二、在季報（半年報、年報）公告後公佈的財務預測，好消息會引起融資顯著增加，融券增加幅度雖不如融黃大，但結果亦為顯著；壞消息會使融資及融券同樣顯著增加，但融資增加幅度亦較融券顯著。 / This study aims to examine the relationship between an announcement of voluntary forecasts and credit transactions, including margin and short transactions. In general, an announcement of good news would attract investor to employ margin for a long position, and vice versa. Since only noisy trader can employ credit transaction in Taiwan, this study hypothesizes that investors would follow the announcement for making rational expectation. The results of this study could help understand how noisy traders use a financial forecast. This study selects the samples occurred between 1995 and 1997 to test the established hypotheses. 　　The empirical results can be summarized as follows. 　　●If the announcement of voluntary forecast occurred prior to the release of quarterly, semiannual, and annual reports, both good and bad news simultaneously cause an increase of margin and short transactions during this period. However, the magnitude of margin transactions is significantly higher than that of short transactions. 　　●If the announcement of voluntary forecast occurred subsequent to the release of quarterly, semiannual, and annual reports, both good and bad news simultaneously cause an increase of margin and short transactions during this period; however, the magnitude of margin transaction is significantly higher than that of short transaction. 　　Since noisy traders are essentially information followers, their judgement significantly relates to functional efficiency of informational intermediaries. These empirical results imply the function of informational intermediaries requires further improvement.

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There is high demand for computational tools that can automatically label tweets (Twitter messages) as having positive or negative sentiment, but great effort and expense would be required to build a large enough hand-labeled training corpus on which to apply standard machine learning techniques. Going beyond current keyword-based heuristic techniques, this paper uses emoticons (e.g. ':)' and ':(') to collect a large training set with noisy labels using little human intervention and trains a Maximum Entropy classifier on that training set. Results on two hand-labeled test corpora are compared to various baselines and a keyword-based heuristic approach, with the machine learned classifier significantly outperforming both. / text

Thesis (MScEng) -- Stellenbosch University, 2014. / ENGLISH ABSTRACT: The increasing popularity of microblogging services such as Twitter means that more and more unstructured data is available for analysis. The informal language usage in these media presents a problem for traditional text mining and natural language processing tools. We develop a pre-processor to normalise this noisy text so that useful information can be extracted with standard tools. A system consisting of a tokeniser, out-of-vocabulary token identifier, correct candidate generator, and N-gram language model is proposed. We compare the performance of generative and discriminative probabilistic models for these different modules. The effect of normalising the training and testing data on the performance of a tweet sentiment classifier is investigated. A linear-chain conditional random field, which is a discriminative model, is found to work better than its generative counterpart for the tokenisation module, achieving a 0.76% character error rate compared to 1.41% for the finite state automaton. For the candidate generation module, however, the generative weighted finite state transducer works better, getting the correct clean version of a word right 36% of the time on the first guess, while the discriminatively trained hidden alignment conditional random field only achieves 6%. The use of a normaliser as a pre-processing step does not significantly affect the performance of the sentiment classifier. / AFRIKAANSE OPSOMMING: Mikro-webjoernale soos Twitter word al hoe meer gewild, en die hoeveelheid ongestruktureerde data wat beskikbaar is vir analise groei daarom soos nooit tevore nie. Die informele taalgebruik in hierdie media maak dit egter moeilik om tradisionele tegnieke en bestaande dataverwerkingsgereedskap toe te pas. ’n Stelsel wat hierdie ruiserige teks normaliseer word ontwikkel sodat bestaande pakkette gebruik kan word om die teks verder te verwerk. Die stelsel bestaan uit ’n module wat die teks in woordeenhede opdeel, ’n module wat woorde identifiseer wat gekorrigeer moet word, ’n module wat dan kandidaat korreksies voorstel, en ’n module wat ’n taalmodel toepas om die mees waarskynlike skoon teks te vind. Die verrigting van diskriminatiewe en generatiewe modelle vir ’n paar van hierdie modules word vergelyk en die invloed wat so ’n normaliseerder op die akkuraatheid van ’n sentimentklassifiseerder het word ondersoek. Ons bevind dat ’n lineêre-ketting voorwaardelike toevalsveld—’n diskriminatiewe model — beter werk as sy generatiewe eweknie vir tekssegmentering. Die voorwaardelike toevalsveld-model behaal ’n karakterfoutkoers van 0.76%, terwyl die toestandsmasjien-model 1.41% behaal. Die toestantsmasjien-model werk weer beter om kandidaat woorde te genereer as die verskuilde belyningsmodel wat ons geïmplementeer het. Die toestandsmasjien kry 36% van die tyd die regte weergawe van ’n woord met die eerste raaiskoot, terwyl die diskriminatiewe model dit slegs 6% van die tyd kan doen. Laastens het ons bevind dat die vooraf normalisering van Twitter boodskappe nie ’n beduidende effek op die akkuraatheid van ’n sentiment klassifiseerder het nie.

Voice control is a technology that most people encounter or use on a daily basis. The voice control technology can be used to interpret voice commands and execute tasks based on the command pronounced. According to previous studies problems arise with the precision when the voice control technologies are used in noisy environments. This study has been conducted as an experiment where the precision in two voice control frameworks for Android has been examined. The purpose with this study is to examine the precision in these two frameworks to assist a decision making for an organisation who has developed an application which will be used by midwives in low and middle income countries. Two prototypes was developed using the two voice control frameworks PocketSphinx and iSpeech. The precision of these frameworks was tested in three different surroundings. The surroundings the frameworks was tested in had the decibel levels 25, 60, and 80. The result shows that the number of correctly registered voice commands reduces considerably depending on which sound level the frameworks are being tested in. The framework who got the most voice commands correctly registered was PocketSphinx, but even this framework had a big margin of error. / Röststyrning är idag en teknologi som de flesta människor någon gång stöter på eller använder sig av dagligen. Röststyrningsteknologin kan användas för att tolka vissa kommandon som sedan utför en uppgift baserat på det kommando som uttalas. Enligt tidigare studier uppkommer det problem med precisionen hos de röststyrningsramverk som används i bullriga miljöer. Denna studie har utförts som ett experiment där precisionen hos två stycken röststyrningsramverk för Android har undersökts. Syftet med denna studie var att undersöka precisionen hos dessa ramverk för att bistå med underlag till en organisation som utvecklat en applikation som används av barnmorskor i låg- och medelinkomstländer. Två stycken prototyper utvecklades med hjälp av röststyrningsramverken PocketSphinx och iSpeech. Dessa ramverks precision testades i tre stycken olika miljöer. De miljöer som prototyperna testades i hade ljudnivåerna 25dB, 60dB samt 80dB. Resultatet påvisar att antalet korrekt registrerade kommandon minskar avsevärt beroende på vilken ljudnivå som ramverken testas i. Det ramverk som korrekt registrerade flest röstkommandon var PocketSphinx men även denna hade en stor felmarginal.

We consider the problem of reconstructing a surface from noisy samples by approximating the point set with non-uniform rational B-spline surfaces. We focus on the fact that the knot sequences should also be part of the unknown variables that include the control points and the weights in order to find their optimal positions. We show how to set up the free knot problem such that constrained nonlinear optimization can be applied efficiently. We describe in detail a parallel implementation of our approach that give almost linear speedup. Finally, we provide numerical results obtained on the Chemnitzer Linux Cluster supercomputer.

This thesis explores and compares different methods of optimizing queries in Bayesian networks. Bayesian networks are graph-structured models that model probabilistic variables and their influences on each other; a query poses the question of what probabilities certain variables assume, given observed values on certain other variables. Bayesian inference (calculating these probabilities) is known to be NP-hard in general, but good algorithms exist in practice. Inference optimization traditionally concerns itself with finding and tweaking efficient algorithms, and leaves the choice of algorithms' parameters, as well as the construction of inference-friendly Bayesian network models, as an exercise to the end user. This thesis aims towards a more systematic approach to these topics: We try to optimize the structure of a given Bayesian network for inference, also taking into consideration what is known about the kind of queries that are posed. First, we implement several automatic model modifications that should help to make a model more suitable for inference. Examples of these are the conversion of definitions of conditional probability distributions from table form to noisy gates, and divorcing parents in the graph. Second, we introduce the concepts of usage profiles and query interfaces on Bayesian networks and try to take advantage of them. Finally, we conduct performance measurements of the different options available in the used library for Bayesian networks, to compare the effects of different options on speedup and stability, and to answer the question of which options and parameters represent the optimal choice to perform fast queries in the end product. The thesis gives an overview of what issues are important to consider when trying to optimize an application's query performance in Bayesian networks, and when trying to optimize Bayesian networks for queries. The project uses the SMILE library for Bayesian networks by the University of Pittsburgh, and includes a case study on script-generated Bayesian networks for troubleshooting by Scania AB.