Abstract The purpose of the study is to propose an optimum combination waste management reduce GHG emissions while ensuring feasible economic benefits through calculation BCR value using MD technique A Simplex Centroid Design consisting three possible treatments, which are composting, reuse, and recycling. recovery factor each treatment component was analyzed calculate emission. result showed t...

This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opinion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time serie...

CAT scanning is used in longitudinal animal science experiments to assess possible changes to carcase composition induced by treatment over given time periods. A hierarchical Bayesian mixture model can be used to analyse the CAT scan data in terms of the proportion of each tissue type present in a scan. In this paper we present an extension to the hierarchical Bayesian mixture model in which es...

Object descriptor has become one of the key factors for a robust and accurate tracker. In this paper, we propose an object descriptor combining color information and motion detection. A tracked object can be described by its hue histogram excluding the background pixels around the tracked object for restraining the disturbing of complex background environments. During the tracking process, we m...

Microarrays have become increasingly common in biological and medical research. They enable the simultaneous study of thousands of genes and provide gene expression information on a whole genome level. A major goal of microarray experiments is to determine which genes are differentially expressed between samples. A mixed model approach using the Johnson’s system of distributions and Baye’s form...

In this paper, we propose an experimental method for building a specialized corpus for training and evaluating backchannel prediction models of spoken dialogue. To develop a backchannel prediction model using a machine learning technique, it is necessary to discriminate between the timings of the interlocutor’s speech when more listeners commonly respond with backchannels and the timings when f...

We develop a general method to match unstructured text reviews to a structured list of objects. For this, we propose a language model for generating reviews that incorporates a description of objects and a generic review language model. This mixture model gives us a principled method to find, given a review, the object most likely to be the topic of the review. Extensive experiments and analysi...

Different models of categorization are often treated as competing accounts, but specific models are often used to understand individual differences, by estimating individual-level parameters. We develop an approach to understanding categorization that allows for individual differences both between and within models, using two prominent categorization models that make different theoretical assum...

We develop a method for the decomposition of structural brain connectivity estimates into locally coherent components, leveraging a non-parametric Bayesian hierarchical mixture model with tangent Gaussian components. This model provides a mechanism to share information across subjects while still including explicit mixture distributions of connections for each subject. It further uses mixture c...

In this paper, we propose a method for image clustering using multinomial mixture models. The mixture of multinomial distributions, often called multinomial mixture, is a probabilistic model mainly used for text mining. The effectiveness of multinomial distribution for text mining originates from the fact that words can be regarded as independently generated in the first approximation. In this ...