We present an audio chord recognition system based on a generalization of the Hidden Markov Model (HMM) in which the duration of chords is explicitly considered a type of HMM referred to as a hidden semi-Markov model, or duration-explicit HMM (DHMM). We find that such a system recognizes chords at a level consistent with the state-of-the-art systems – 84.23% on Uspop dataset at the major/minor ...

This paper shows how to use labeled and unlabeled data to improve inductive models with the help of transductive models. We proposed a solution for the self-training scenario. Selftraining is an effective semi-supervised wrapper method which can generalize any type of supervised inductive model to the semi-supervised settings. it iteratively refines a inductive model by bootstrap from unlabeled...

The problem of node classification in networks is an important one in a wide variety of social networking domains. In many real applications such as product recommendations, the class of interest may be very rare. In such scenarios, it is often very difficult to learn the most relevant node classification characteristics, both because of the paucity of training data, and because of poor connect...

Modern network traffic classification approaches apply machine learning techniques to statistical flow properties, allowing accurate classification even when traditional approaches fail. We base our approach to the task on a state-of-the-art semi-supervised classifier to identify known and unknown flows with little labelled training data. We propose a new algorithm for mapping clusters to class...

The web has become the central platform and marketplace for the organization, propagation of events and sale of tickets of any kind. Such events range from concerts, workshops, sport events, professional events to small local events. Individual’s event choices vary tremendously based on preferences and lifestyle. Online users use the web to inform themselves about new events near their location...

This paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervise...

Isolation of Campylobacter jejuni and C. coli from stool specimens is done by growing campylobacter colonies on solid selective media with or without blood. However, recognition of these colonies can be difficult. Therefore, we decided to evaluate an isolation procedure based on the swarming of campylobacters through a semisolid medium. We developed a semisolid blood-free selective motility (SS...

Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically charact...

Classification of partially labeled data requires linking the unlabeled input distribution P (x) with the conditional distribution P (y|x) obtained from the labeled data. The latter should, for example, vary little in high density regions. The key problem is to articulate a general principle behind this and other such reasonable assumptions. In this paper we provide a new approach to semisuperv...

It is often tedious and expensive to label large training data sets for learning-based object and scene recognition systems. This problem could be alleviated by semi-supervised learning techniques, which can automatically select more training samples from unlabel data for reducing the cost of labeling. In this paper, we proposed a multi-class co-training learning method of two different views f...