Hierarchical semi-markov conditional random fields for recursive sequential data

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.<br /

Hybrid Sentiment Classification of Reviews Using Synonym Lexicon and Word embedding

Sentiment analysis is used in extract some useful information from the given set of documents by using Natural Language Processing (NLP) techniques. These techniques have wide scope in various fields which are dealing with huge amount of data link e-commerce, business and market analysis, social media and review impact of products and movies. Sentiment analysis can be applied over these data for finding the polarity of the data like positive, neutral or negative automatically or many complex sentiments like happiness, sad, anger, joy, etc. for a particular product and services based on user reviews. Sentiment analysis not only able to find the polarity of the reviews. Sentiment analysis utilizes machine learning algorithms with vectorization techniques based on textual documents to train the classifier models. These models are later used to perform sentiment analysis on the given dataset of particular domain on which the classifier model is trained. Vectorization is done for text document by using word embedding based and hybrid vectorization. The proposed methodology focus on fast and accurate sentiment prediction with higher confidence value over the dataset in both Tamil and English

Evolutionary Granular Kernel Machines

Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently