Multi-learner based recursive supervised training

In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system

Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification

Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%

Master of Science

thesisCurrently, few methods exist to accurately model a human motion inside a monitored area. Most of the approaches that exist depend on some kind of boolean data from sensors that tell the presence or absence of person a at a given instant of time near a particular sensor. Using that information, some systems can then track a person across the area at di erent timestamps. Furthermore, for most existing approaches, the accuracy drops rapidly as the number of persons in the image increases. The sensors used in such settings are usually expensive. Not much work has been done to build a similar system based on inexpensive radio sensors. As there is no way for our radio sensors to provide information as to whether a person is present at a location, we need to extract it from the data using computer vision and machine learning techniques. However, it is not easy in such a system to model the noise component accurately. Therefore, we provide a probabilistic model to decide whether a detected blob is noise or an actual person. In our work, we exploit the fact that images do not change by much between successive timeframes and use this to detect and track multiple persons in a monitored area with a reasonably high accuracy. We use location and count of persons in historical images, and their similarity with the current image to calculate the new locations and count