Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

ACCURACY AND MULTI-CORE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION

There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict accurately based on previous examples. The second one is to make these algorithms run fast enough to be able to do real time tasks. The aim of this thesis is to study and compare the accuracy and multi-core performance of some of the best learning algorithms to the task of handwritten character recognition. Seven algorithms are compared for their accuracy on the MNIST database, and the test set accuracy (generalization) for the different algorithms are compared. The second task is to implement and compare the performance of two of the hierarchical Bayesian based cortical algorithms, Hierarchical Temporal Memory (HTM) and Hierarchical Expectation Refinement Algorithm (HERA) on multi-core architectures. The results indicate that the HTM and HERA algorithms can make use of the parallelism in multi-core architectures

Deviating Angular Feature for Image Recognition System Using the Improved Neural Network Classifier.

The ability to recognize images makes it possible to abstractly conceptualize the world. Many in the field of machine learning have attempted to invent an image recognition system with the recognition capabilities of a human. This dissertation presents a method of modifications to existent image recognition systems, which greatly improves the efficiency of existing data imaging methods. This modification, the Deviating Angular Feature (DAF), has two obvious applications: (1) the recognition of handwritten numerals; and (2) the automatic identification of aircraft. Modifications of feature extraction and classification processes of current image recognition systems can leads to the systemic enhancement of data imaging. This research proposes a customized blend of image curvature extraction algorithms and the neural network classifiers trained by the Epoch Gradual Increase in Accuracy (EGIA) training algorithm. Using the DAF, the recognition of handwritten numerals and the automatic identification of aircraft have been improved. According to the preliminary results, the recognition system achieved an accuracy rate of 98.7% when applied to handwritten numeral recognition. When applied to automatic aircraft identification, the system achieved a 100% rate of recognition. The novel design of the prototype is quite flexible; thus, the system is easy to maintain, modify, and distribute

Machine Learning with a Reject Option: A survey

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas

Multiview pattern recognition methods for data visualization, embedding and clustering

Multiview data is defined as data for whose samples there exist several different data views, i.e. different data matrices obtained through different experiments, methods or situations. Multiview dimensionality reduction methods transform a high­dimensional, multiview dataset into a single, low-dimensional space or projection. Their goal is to provide a more manageable representation of the original data, either for data visualization or to simplify the following analysis stages. Multiview clustering methods receive a multiview dataset and propose a single clustering assignment of the data samples in the dataset, considering the information from all the input data views. The main hypothesis defended in this work is that using multiview data along with methods able to exploit their information richness produces better dimensionality reduction and clustering results than simply using single views or concatenating all views into a single matrix. Consequently, the objectives of this thesis are to develop and test multiview pattern recognition methods based on well known single-view dimensionality reduction and clustering methods. Three multiview pattern recognition methods are presented: multiview t-distributed stochastic neighbourhood embedding (MV-tSNE), multiview multimodal scaling (MV-MDS) and a novel formulation of multiview spectral clustering (MVSC-CEV). These methods can be applied both to dimensionality reduction tasks and to clustering tasks. The MV-tSNE method computes a matrix of probabilities based on distances between sam ples for each input view. Then it merges the different probability matrices using results from expert opinion pooling theory to get a common matrix of probabilities, which is then used as reference to build a low-dimensional projection of the data whose probabilities are similar. The MV-MDS method computes the common eigenvectors of all the normalized distance matrices in order to obtain a single low-dimensional space that embeds the essential information from all the input spaces, avoiding redundant information to be included. The MVSC-CEV method computes the symmetric Laplacian matrices of the similaritymatrices of all data views. Then it generates a single, low-dimensional representation of the input data by computing the common eigenvectors of the Laplacian matrices, obtaining a projection of the data that embeds the most relevan! information of the input data views, also avoiding the addition of redundant information. A thorough set of experiments has been designed and run in order to compare the proposed methods with their single view counterpart. Also, the proposed methods have been compared with all the available results of equivalent methods in the state of the art. Finally, a comparison between the three proposed methods is presented in order to provide guidelines on which method to use for a given task. MVSC-CEV consistently produces better clustering results than other multiview methods in the state of the art. MV-MDS produces overall better results than the reference methods in dimensionality reduction experiments. MV-tSNE does not excel on any of these tasks. As a consequence, for multiview clustering tasks it is recommended to use MVSC-CEV, and MV-MDS for multiview dimensionality reduction tasks. Although several multiview dimensionality reduction or clustering methods have been proposed in the state of the art, there is no software implementation available. In order to compensate for this fact and to provide the communitywith a potentially useful set of multiview pattern recognition methods, an R software package containg the proposed methods has been developed and released to the public.Los datos multivista se definen como aquellos datos para cuyas muestras existen varias vistas de datos distintas , es decir diferentes matrices de datos obtenidas mediante diferentes experimentos , métodos o situaciones. Los métodos multivista de reducción de la dimensionalidad transforman un conjunto de datos multivista y de alta dimensionalidad en un único espacio o proyección de baja dimensionalidad. Su objetivo es producir una representación más manejable de los datos originales, bien para su visualización o para simplificar las etapas de análisis subsiguientes. Los métodos de agrupamiento multivista reciben un conjunto de datos multivista y proponen una única asignación de grupos para sus muestras, considerando la información de todas las vistas de datos de entrada. La principal hipótesis defendida en este trabajo es que el uso de datos multivista junto con métodos capaces de aprovechar su riqueza informativa producen mejores resultados en reducción de la dimensionalidad y agrupamiento frente al uso de vistas únicas o la concatenación de varias vistas en una única matriz. Por lo tanto, los objetivos de esta tesis son desarrollar y probar métodos multivista de reconocimiento de patrones basados en métodos univista reconocidos. Se presentan tres métodos multivista de reconocimiento de patrones: proyección estocástica de vecinos multivista (MV-tSNE), escalado multidimensional multivista (MV-MDS) y una nueva formulación de agrupamiento espectral multivista (MVSC-CEV). Estos métodos pueden aplicarse tanto a tareas de reducción de la dimensionalidad como a de agrupamiento. MV-tSNE calcula una matriz de probabilidades basada en distancias entre muestras para cada vista de datos. A continuación combina las matrices de probabilidad usando resultados de la teoría de combinación de expertos para obtener una matriz común de probabilidades, que se usa como referencia para construir una proyección de baja dimensionalidad de los datos. MV-MDS calcula los vectores propios comunes de todas las matrices normalizadas de distancia para obtener un único espacio de baja dimensionalidad que integre la información esencial de todos los espacios de entrada, evitando información redundante. MVSC-CEVcalcula las matrices Laplacianas de las matrices de similitud de los datos. A continuación genera una única representación de baja dimensionalidad calculando los vectores propios comunes de las Laplacianas. Así obtiene una proyección de los datos que integra la información más relevante y evita añadir información redundante. Se ha diseñado y ejecutado una batería de experimentos completa para comparar los métodos propuestos con sus equivalentes univista. Además los métodos propuestos se han comparado con los resultados disponibles en la literatura. Finalmente, se presenta una comparación entre los tres métodos para proporcionar orientaciones sobre el método más adecuado para cada tarea. MVSC-CEV produce mejores agrupamientos que los métodos equivalentes en la literatura. MV-MDS produce en general mejores resultados que los métodos de referencia en experimentos de reducción de la dimensionalidad. MV-tSNE no destaca en ninguna de esas tareas . Consecuentemente , para agrupamiento multivista se recomienda usar MVSC-CEV, y para reducción de la dimensionalidad multivista MV-MDS. Aunque se han propuesto varios métodos multivista en la literatura, no existen programas disponibles públicamente. Para remediar este hecho y para dotar a la comunidad de un conjunto de métodos potencialmente útil, se ha desarrollado un paquete de programas en R y se ha puesto a disposición del público

Lampung handwritten character recognition

Lampung script is a local script from Lampung province Indonesia. The script is a non-cursive script which is written from left to right. It consists of 20 characters. It also has 7 unique diacritics that can be put on top, bottom, or right of the character. Considering this position, the number of diacritics augments into 12 diacritics. This research is devoted to recognize Lampung characters along with diacritics. The research aim to attract more concern on this script especially from Indonesian researchers. Beside, it is also an endeavor to preserve the script from extinction. The work of recognition is administered by multi steps processing system the so called Lampung handwritten character recognition framework. It is started by a preprocessing of a document image as an input. In the preprocessing stage, the input should be distinguished between characters and diacritics. The character is classified by a multistage scheme. The first stage is to classify 18 character classes and the second stage is to classify special characters which consist of two components. The number of classes after the second stage classification becomes 20 class. The diacritic is classified into 7 classes. These diacritics should be associated to the characters to form compound characters. The association is performed in two steps. Firstly, the diacritic detects some characters nearby. The character with closest distance to that diacritic is selected as the association. This is completed until all diacritics get their characters. Since every diacritic already has one-to-one association to a character, the pivot element is switched to a character in the second step. Each character collects all its diacritics as a composition of the compound characters. This framework has been evaluated on Lampung dataset created and annotated during this work and is hosted at the Department of Computer Science, TU Dortmund, Germany. The proposed framework achieved 80.64% recognition rate on this data

Probabilistic and Deep Learning Algorithms for the Analysis of Imagery Data

Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework for the analysis of the NAIP dataset which includes (1) an unsupervised segmentation module based on the Statistical Region Merging algorithm, (2) a feature extraction module that extracts a set of standard hand-crafted texture features from the images, (3) a supervised classification algorithm based on Feedforward Backpropagation Neural Networks, and (4) a structured prediction framework using Conditional Random Fields that integrates the results of the segmentation and classification modules into a single composite model to generate the final class labels. Next, we introduce two new datasets SAT-4 and SAT-6 sampled from the NAIP imagery and use them to evaluate a multitude of Deep Learning algorithms including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) for generating class labels. Finally, we propose a learning framework by integrating hand-crafted texture features with a DBN. A DBN uses an unsupervised pre-training phase to perform initialization of the parameters of a Feedforward Backpropagation Neural Network to a global error basin which can then be improved using a round of supervised fine-tuning using Feedforward Backpropagation Neural Networks. These networks can subsequently be used for classification. In the following discussion, we show that the integration of hand-crafted features with DBN shows significant improvement in performance as compared to traditional DBN models which take raw image pixels as input. We also investigate why this integration proves to be particularly useful for aerial datasets using a statistical analysis based on Distribution Separability Criterion. Then we introduce a new dataset called noisy-MNIST (n-MNIST) by adding (1) additive white gaussian noise (AWGN), (2) motion blur and (3) Reduced contrast and AWGN to the MNIST dataset and present a learning algorithm by combining probabilistic quadtrees and Deep Belief Networks. This dynamic integration of the Deep Belief Network with the probabilistic quadtrees provide significant improvement over traditional DBN models on both the MNIST and the n-MNIST datasets. Finally, we extend our experiments on aerial imagery to the class of general texture images and present a theoretical analysis of Deep Neural Networks applied to texture classification. We derive the size of the feature space of textural features and also derive the Vapnik-Chervonenkis dimension of certain classes of Neural Networks. We also derive some useful results on intrinsic dimension and relative contrast of texture datasets and use these to highlight the differences between texture datasets and general object recognition datasets