Forecasting stock market behavior has received tremendous attention from investors, and researchers for a very long time due to its potential profitability. Predicting is regarded as one of the extremely challenging applications series forecasting. While there divided opinion on efficiency markets, numerous empirical studies which are widely accepted have shown that predictable some extent. Sta...

In this paper, we study the classification problem in the situation where large volumes of training data become available sequentially (online learning). In medical imaging, this is typical, e.g., a 3D brain MRI dataset may be gradually collected from a patient population, and not all of the data is available when the analysis begins. First, we describe two common ensemble learning algorithms, ...

We present a novel clustering approach, that exploits boosting as the primary means of modelling clusters. Typically, boosting is applied in a supervised classification context; here, we move in the less explored unsupervised scenario. Starting from an initial partition, clusters are iteratively re-estimated using the responses of one-vs-all boosted classifiers. Within-cluster homogeneity and s...

We propose an ensemble learning method called Network Boosting which combines weak learners together based on a random graph (network). A theoretic analysis based on the game theory shows that the algorithm can learn the target hypothesis asymptotically. The comparison results using several datasets of the UCI machine learning repository and synthetic data are promising and show that Network Bo...

Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent...

We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In particular, for any set of m labeled examples consistent with a disjunction of k literals (which are one-sided in this case), AdaBoost constructs a consistent hypothesis by using O(k logm) iterations. On the other hand, ...

Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too “dull” to handle such imbalanced classification. In this paper, we improve dete...

In this paper we apply multi-armed bandits (MABs) to improve the computational complexity of AdaBoost. AdaBoost constructs a strong classifier in a stepwise fashion by selecting simple base classifiers and using their weighted “vote” to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs where each arm ...

Declaration All sentences or passages quoted in this dissertation from other people's work have been specifically acknowledged by clear cross-referencing to author, work and page(s). Any illustrations which are not the work of the author of this dissertation have been used with the explicit permission of the originator and are specifically acknowledged. I understand that failure to do this amou...

The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition. Assumptions are given for the existence of the influence function of the classifiers and for bounds of the influence function. Kernel logistic regression...