This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Please provide a short abstract of 100 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. Work planned but not completed should not appear in the abstract.

User Modeling and User-Adapted Interaction

Thank you for reading software product line engineering foundations principles and techniques. Maybe you have knowledge that, people have look numerous times for their favorite books like this software product line engineering foundations principles and techniques, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their desktop computer.

Software Product Line Engineering Foundations Principles And Techniques

Diversification has become one of the leading topics of recommender system research not only as a way to solve the over-fitting problem but also an approach to increasing the quality of the users experience with the recommender system. This article aims to provide an overview of research done on this topic from one of the first mentions of diversity in 2001 until now. The articles ,and research, have been divided into three sub-topics for a better overview of the work done in the field of recommendation diversification: the definition and evaluation of diversity; the impact of diversification on the quality of recommendation results and the development of diversification algorithms themselves. In this way, the article aims both to offer a good overview to a researcher looking for the state-of-the-art on this topic and to help a new developer get familiar with the topic.

Diversity in recommender systems A survey

https://decsai.ugr.es/~lci/journal-papers-pdf/ijar10-hybrid.pdf

Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

A dynamic gesture recognition and prediction system using the convexity approach

This paper presents a hybrid MLP-SVM method for cursive characters recognition. Specialized Support Vector Machines (SVMs) are introduced to significantly improve the performance of Multilayer Perceptron (MLP) in the local areas around the surfaces of separation between each pair of characters in the space of input patterns. This hybrid architecture is based on the observation that when using MLPs in the task of handwritten characters recognition, the correct class is almost always one of the two maximum outputs of the MLP. The second observation is that most of the errors consist of pairs of classes in which the characters have similarities (e.g. (U, V), (m, n), (O, Q), among others). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer showed improvement, significant, in performance in terms of recognition rate compared with an MLP for a task of character recognition.

A KNN-SVM hybrid model for cursive handwriting recognition

A symbolic approach for content-based information filtering

One of the main challenges in the handwriting recognition area lies in identifying complete lines of handwritten text. In this paper, we propose a handwriting recognition system based on a deep multidimensional long-short-term memory (MDLSTM) network within a hybrid hidden Markov model framework. The MDLSTM architecture was elaborated to enhance the recognition performance and decrease the recognition time. Accordingly, we present modifications regarding the layers order and the number of pooling layers compared to a standard MDLSTM model. Since the results reported in the literature for deeper MDLSTM architectures relies on optimizing the network width with a fixed depth, we investigate the trade-off between both these properties to obtain an optimal topology. The system was evaluated with English handwritten text lines from the IAM database and the experiments demonstrated that the proposed MDLSTM architecture was able to maintain a robust recognition performance (around 3.6% CER and 10.5% WER) and present significant speedups, approximately 48% and 32% faster than the state-of-the-art MDLSTM optical model, regarding the learning and classification times, respectively. The full system including a decoder with linguistic knowledge presents competitive results with the state-of-the-art.

Boosting the Deep Multidimensional Long-Short-Term Memory Network for Handwritten Recognition Systems

Recommender systems have become an important tool to cope with the information overload problem by acquiring data about user behavior. After tracing the user’s behavior, through actions or rates, computational recommender systems use information- filtering techniques to recommend items. In order to recommend new items, one of the three major approaches is generally adopted: content-based filtering, collaborative filtering, or hybrid filtering. This paper presents three information-filtering methods, each of them based on one of these approaches. In our methods, the user profile is built up through symbolic data structures and the user and item correlations are computed through dissimilarity functions adapted from the symbolic data analysis (SDA) domain. The use of SDA tools has improved the performance of recommender systems, particularly concerning the find good items task measured by the half-life utility metric, when there is not much information about the user.

/pdf/symbolic-data-analysis-tools-for-recommendation-systems-1bjkl809q8.pdf

Byron L. D. Bezerra

Papers

A dynamic gesture recognition and prediction system using the convexity approach

A KNN-SVM hybrid model for cursive handwriting recognition

A symbolic approach for content-based information filtering

Boosting the Deep Multidimensional Long-Short-Term Memory Network for Handwritten Recognition Systems

Symbolic data analysis tools for recommendation systems