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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,...
Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards...
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering method based on semi-supervised learning to get focuses of social topics in a large...
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...
We have established a multi-walker recognition/tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (Rol) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data...
K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seed based K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or they are assumed to be available for each cluster....
Ant Colony Optimization (ACO), an inspired algorithm from nature, has been successfully applied to classification tasks of data mining in recent years. This paper proposes a rule-based system for medical data mining by using a combination of ACO and fuzzy set theory, named FACO-Miner. FACO-Miner utilizes an ACO algorithm to learn a set of fuzzy rules from labeled data in parallel manner which causes...
Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All existing approaches, however, are based on an assessment of distances (sometimes indirectly by assuming certain distributions) in the full-dimensional Euclidean data space. In high-dimensional data, these approaches are...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
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...
Q-learning is a machine learning technique that learns what to do and how to map states to actions to maximize rewards. Q-learning has been applied to various tasks such as foraging, soccer and prey-pursuing robots. In this paper, a simple foraging task has been considered to study the influences of the policies reported in the open literatures. A mobile robot is used to search and retrieve pucks...
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relational databases exhibit rich graphical structure and are naturally modeled as graphs whose nodes represent entities and whose typed-edges represent relations between them. We propose using random walk models for resolving...
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant...
Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle,...
In this paper, we propose a new iterative learning control (ILC) scheme, which is devoted to dealing with unknown parameters that are both time varying and iteration varying. In particular, we consider iteration-varying parameters that are generated by a second-order internal model. By incorporating the internal model into the parametric learning law, the ILC scheme can handle more generic nonlinear...
We describe a system that successfully transfers value function knowledge across multiple subdomains of real-time strategy games in the context of multiagent reinforcement learning. First, we implement an assignment-based decomposition architecture, which decomposes the problem of coordinating multiple agents into the two levels of task assignment and task execution. Second, a hybrid model-based approach...
In this paper, we address the problem of recovering an optimal salient image descriptor transformation for image classification. Our method involves two steps. Firstly, a binary salient map is generated to specify the regions of interest for subsequent image feature extraction. To this end, an optimal cut-off value is recovered by maximising Fisher's linear discriminant separability measure so as...
This paper presents an algorithm for object localization and segmentation. The algorithm uses machine learning, and statistical and combinatorial optimization tools to build a tracker that is robust to noise and occlusions. The method is based on a novel energy formulation and its dual use for object localization and segmentation. The energy uses kernel principal component analysis to incorporate...
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