Word Embedding, Neural Networks and Text Classification: What is the State-of-the-Art?

In this bachelor thesis, I first introduce the machine learning methodology of text classification with the goal to describe the functioning of neural networks. Then, I identify and discuss the current development of Convolutional Neural Networks and Recurrent Neural Networks from a text classification perspective and compare both models. Furthermore, I introduce different techniques used to translate textual information in a language comprehensible by the computer, which ultimately serve as inputs for the models previously discussed. From there, I propose a method for the models to cope with words absent from a training corpus. This first part has also the goal to facilitate the access to the machine learning world to a broader audience than computer science students and experts. To test the proposal, I implement and compare two state-of-the-art models and eight different word representations using pre-trained vectors on a dataset given by LogMeIn and on a common benchmark. I find that, with my configuration, Convolutional Neural Networks are easier to train and are also yielding better results. Nevertheless, I highlight that models that combine both architectures can potentially have a better performance, but need more work on identifying appropriate hyperparameters for training. Finally, I find that the efficacy of word embedding methods depends not only on the dataset but also on the model used to tackle the subsequent task. In my context, they can boost performance by up to 10.2% compared to a random initialization. However, further investigations are necessary to evaluate the value of my proposal with a corpus that contains a greater ratio of unknown relevant words. Keywords: neural networks; machine learning; word embedding; text classification; business analytic

Development of genetic algorithm based classification and cluster analysis methods for analytical data

Thesis (Doctoral)--İzmir Institute of Technology, Chemistry, İzmir, 2009Includes bibliographical references (leaves: 151-158)Text in English; Abstract: Turkish and Englishxviii, 158 leavesIn this study genetic algorithm based classification and clustering methods were aimed to develop for the spectral data. The developed methods were completely achieved hybridization of nature inspired algorithm (genetic algorithms, GAs) to other classification or clustering methods. The first method was genetic algorithm based principal component analysis (GAPCAD), and the second was genetic algorithm based discriminant analysis (GADA). Both methods were performed to achieve the best discrimination between the olive oil and vegetable oil samples. The classifications of samples were examined directly from their spectral data obtained from using near infrared spectrometry, Fourier transform infrared (FTIR) spectrometry, and spectrofluorometry. The GA was used to optimize the performance of classification or clustering techniques. on training set in order to maximize the correct classification of acceptable and unacceptable samples or samples of dissimilar properties and to reduce the spectral data by wavelength selection. After GA optimization the classification results of training set were controlled by validation set. Lastly, the success of both algorithms was compared to the results of PCA and SIMCA

Magellan : un agent pour simplifier les achats sur internet

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Towards a unified treatment of 3D display using partially coherent light

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 111-120).This thesis develops a novel method of decomposing a 3D phase space description of light into multiple partially coherent modes, and applies this decomposition to the creation of a more flexible 3D display format. Any type of light, whether it is completely coherent, partially coherent or incoherent, can be modeled either as a sum of coherent waves or as rays. A set of functions, known as phase space functions, provide an intuitive model for these waves or rays as they pass through a 3D volume to a display viewer's eyes. First, this thesis uses phase space functions to mathematically demonstrate the limitations of two popular 3D display setups: parallax barriers and coherent holograms. Second, this thesis develops a 3D image design algorithm based in phase space. The "mode-selection" algorithm can find an optimal holographic display setup to create any desired 3D image. It is based on an iterative algebraic-rank restriction process, and can be extended to model light with an arbitrary degree of partial coherence. Third, insights gained from partially coherent phase space representations lead to the suggestion of a new form of 3D display, implemented with multiple time-sequential diffracting screens. The mode-selection algorithm determines an optimal set of diffracting screens to display within the flicker-fusion rate of a viewer's eye. It is demonstrated both through simulation and experiment that this time-sequential display offers improved performance over a fixed holographic display, creating 3D images with increased intensity variation along depth. Finally, this thesis investigates the tradeoffs involved with multiplexing a holographic display over time with well-known strategies of multiplexing over space, illumination angle and wavelength. The examination of multiplexing tradeoffs is extended into the incoherent realm, where comparisons to ray-based 3D displays can hopefully offer a more unified summary of the limitations of controlling light within a volume.by Roarke Horstmeyer.S.M