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Inverse Reinforcement Learning (IRL) is an approach for domain-reward discovery from demonstration, where an agent mines the reward function of a Markov decision process by observing an expert acting in the domain. In the standard setting, it is assumed that the expert acts (nearly) optimally, and a large number of trajectories, i.e., training examples are available for reward discovery (and consequently,...
This paper presents a novel framework for multi-folder email classification using graph mining as the underlying technique. Although several techniques exist (e.g., SVM, TF-IDF, n-gram) for addressing this problem in a delimited context, they heavily rely on extracting high-frequency keywords, thus ignoring the inherent structural aspects of an email (or document in general) which can play a critical...
Data mining is "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data." Data mining is an inter-disciplinary field, whose core is at the intersection of machine learning, statistics and databases. A major objective of this work is to evaluate data mining tools in medical and health care applications to develop a tool that can help make timely...
One of the main preprocessing steps for having a high performance text classifier is feature weighting. Commonly used feature weighting methods such as TF and IDF-based methods only consider the distribution of a feature in the document(s) and do not consider class information for feature weighting. In this paper, we present TFCRF (Term Frequency and Category Relevancy Factor) method in which the...
Text classification poses some specific challenges. One such challenge is its high dimensionality where each document (data point) contains only a small subset of them. In this paper, we propose semi-supervised impurity based subspace clustering (SISC) in conjunction with k-nearest neighbor approach, based on semi-supervised subspace clustering that considers the high dimensionality as well as the...
Existing fuzzy and neural-fuzzy systems in the literature can be classified into three main categories, i.e. Mamdani, Takagi-Sugeno (T-S) or Tsukamoto systems based on their implemented fuzzy rule structures. Furthermore, depending on the intended modeling objective, there are two main approaches to fuzzy and neural-fuzzy modeling; namely: linguistic fuzzy modeling (LFM) and precise fuzzy modeling...
Understanding the meaning of queries is a key task queries is a challenging task due to the fact that queries are usually short and often ambiguous. A common approach to tackle the problem of short and noisy queries is to enrich the queries. Various enrichment strategies have been proposed that are based on either pseudo-relevance feedback or secondary sources of information. In general, pseudo-relevance...
This paper reports our experiment on applying Q Learning algorithm for learning to play Tic-tac-toe. The original algorithm is modified by updating the Q value only when the game terminates, propagating the update process from the final move backward to the first move, and incorporating a new update rule. We evaluate the agent performance using full-board and partial-board representations. In this...
In this paper we establish the completion of a previously published work, in part by the same authors, in which we proposed a novel learning algorithm involving self organizing map's (SOM) internal structure in the learning process. We present a statistical and a behavioral study of our proposed solution, and confirm its results on a breast cancer classification application. After establishing the...
The natural immune system is composed of cells and molecules with complex interactions. Jerne modeled the interactions among immune cells and molecules by introducing the immune network. The immune system provides an effective defense mechanism against foreign substances. This system like the neural system is able to learn from experience. In this paper, the Jerne's immune network model is extended...
Dimensionality Reduction is a key issue in many scientific problems, in which data is originally given by high dimensional vectors, all of which lie however over a fewer dimensional manifold. Therefore, they can be represented by a reduced number of values that parametrize their position over the mentioned non-linear manifold. This dimensionality reduction is essential not only for representing and...
Neural networks are relatively successful in recognizing individual patterns. However, when images consist of combination of patterns, a preprocessing step of segmentation is required to avoid combinatorial explosion of the training phase. In practical applications, segmentation is a context dependent task which itself requires recognition. In this paper we propose and develop a biologically inspired...
In this paper we present a new fuzzy classification method based on support vector machine (SVM) to treat multi-class problems. Generally, SVMs classifiers are designed to solve binary classification problem. In order to handle multi-class classification problem, we present a new method to build dynamically a fuzzy hierarchical structure from the training data. Our method is based on two main concepts:...
This work consists on the evaluation of the performances of three neural classifiers. The Multi-Layer Perceptron (MLP), the Self-Organizing Map (SOM), the Learning Vector Quantization (LV Q) are considered by this study. The example that will be considered in the evaluation of the technical classifications's performances is the handwritten character recognition.
Recently a classifier was proposed that was based on the assumption: the training samples for a particular class form a linear basis for any new test sample. This assumption is a generalization of the nearest neighbour classifier. In the previous work, the classifier was built upon this assumption required solving a complex optimisation problem. The optimisation method was time consuming and restrictive...
A simple static cascade-forward back-propagation artificial neural network (ANN) is utilized to forecast the effect of stock repurchase on the closing price of firm's common stock. The input factors are composed of today's closing price, index of the stock market and the amount of tomorrow-intended repurchase. A rule-based data clustering is used to group the repurchase days by selecting two records...
In this paper, we propose an algorithm for learning a general class of similarity measures for kNN classification. This class encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. The algorithm we propose is an extension of the voted perceptron algorithm and allows one to learn different types of similarity functions (either based on diagonal, symmetric...
Anti-virus systems traditionally use signatures to detect malicious executables, but this method beyond the capability of many existing detection approaches. In this paper, we present a data mining approach to detect unknown malicious executables. The feature set is a key to applying data mining or machine learning to successfully detect malicious executables. We propose a method to extract features...
Inductive method of model self-organization (IMMSO) developed in 80s by A. Ivakhnenko is an evolutionary machine learning algorithm, which allows selecting a model of optimal complexity that describes or explains a limited number of observation data when any a priori information is absent or is highly insufficient. In this paper, we study the performance of IMMSO to reveal a model in a given class...
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