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Browsing by Author "Agovic, Amrudin"

Now showing 1 - 5 of 5
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    A Unified View of Graph-based Semi-Supervised Learning: Label Propagation, Graph-Cuts, and Embeddings
    (2009-05-12) Agovic, Amrudin; Banerjee, Arindam
    Recent years have seen a growing number of graph-based semi-supervised learning methods. While the literature currently contains several of these methods, their relationships with one another and with other graph-based data analysis algorithms remain unclear. In this paper, we present a unified view of graph-based semi-supervised learning. Our framework unifies three important and seemingly unrelated approaches to semi-supervised learning, viz label propagation, graph cuts and manifold embeddings. We show that most existing label propagation methods solve a special case of a generalized label propagation (GLP) formulation which is a constrained quadratic program involving a graph Laplacian. Different methods arise simply based on the choice of the Laplacian and the nature of the constraints. Further, we show that semi-supervised graph-cut problems can also be viewed and solved as special cases of the GLP formulation. In addition, we show that semi-supervised non-linear manifold embedding methods also solve variants of the GLP problem and propose a novel family of semi-supervised algorithms based on existing embedding methods. Finally, we present comprehensive empirical performance evaluation of the existing label propagation methods as well as the new ones derived from manifold embedding. The new family of embedding based label propagation methods are found to be competitive on several datasets.
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    Component-based design for a trading agent
    (2004-05-04) Collins, John; Agovic, Amrudin; Damer, Steven; Gini, Maria
    MinneTAC is an agent designed to compete in the Supply-Chain Trading Agent Competition. It is also designed to support the needs of a group of researchers, each of whom is interested in different decision problems related to the competition scenario. The design of MinneTAC breaks out each basic behavior into a separate, configurable component. Dependencies between components are almost non-existent. The agent itself is defined as a set of "roles", and a working agent is one for which a component is supplied for each role. This allows each user to focus on a single problem and work independently, and it allows multiple user to tackle the same problem in different ways. A working MinneTAC agent is completely defined by a set of configuration files that map the desired roles to the code that implements them, and that set parameters for the components. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment.
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    Design and Analysis of the MinneTAC-03 Supply-Chain Trading Agent
    (2004-04-26) Ketter, Wolfgang; Kryzhnyaya, Elena; Damer, Steven; McMillen, Colin; Agovic, Amrudin; Collins, John; Gini, Maria
    MinneTAC is an agent designed to compete in the Supply-Chain Trading Agent Competition. It is also designed to support the needs of a group of researchers, each of whom is interested in different decision problems related to the competition scenario. The design of MinneTAC breaks outeach basic behavior into a separate, configurable component. Dependencies between components are almost non-existent. This design allows each user to focus on a single problem and work independently, and it allows multiple user to tackle the same problem in different ways. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment. We also describe two sales strategies used by MinneTAC. Both strategies estimate, as the game progresses, the probability of receiving a customer order for different prices and compute the expected profit. Offers are made to maximize the expected profit on each order. The maindifference between the two strategies is in how the probability of receiving an order and the offer prices are computed. The first strategy works well in high-demand games, the second was developed to improve performance in low-demand games. We empirically analyze the effect of the discount given by suppliers on orders received the firstday of the game, and we show that in high-demand games there is a strong correlation between the offers an agent receives from suppliers on the first day of the game and the agent's performance in the game.
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    Predictive modeling using dimensionality reduction and dependency structures
    (2011-07) Agovic, Amrudin
    As a result of recent technological advances, the availability of collected high dimensional data has exploded in various fields such as text mining, computational biology, health care and climate sciences. While modeling such data there are two problems that are frequently faced. High dimensional data is inherently difficult to deal with. The challenges associated with modeling high dimensional data are commonly referred to as the "curse of dimensionality." As the number of dimensions increases the number of data points necessary to learn a model increases exponentially. A second and even more difficult problem arises when the observed data exhibits intricate dependencies which cannot be neglected. The assumption that observations are independently and identically distributed (i.i.d.) is very widely used in Machine Learning and Data Mining. Moving away from this simplifying assumption with the goal to model more intricate dependencies is a challenge and the main focus of this thesis. In dealing with high dimensional data, dimensionality reduction methods have proven very useful. Successful applications of non-probabilistic approaches include Anomaly Detection, Face Detection, Pose Estimation, and Clustering. Probabilistic approaches have been used in domains such as Visualization, Image retrieval and Topic Modeling. When it comes to modeling intricate dependencies, the i.i.d. assumption is seldomly abandoned. As a result of the simplifying assumption relevant dependencies tend to be broken. The goal of this work is to address the challenges of dealing with high dimensional data while capturing intricate dependencies in the context of predictive modeling. In particular we consider concepts from both non-probabilistic and probabilistic dimensionality reduction approaches.
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    Software architecture of the MinneTAC supply-chain trading agent
    (2008-10-20) Collins, John; Ketter, Wolfgang; Gini, Maria; Agovic, Amrudin
    The MinneTAC trading agent is designed to compete in the Supply-Chain Trading Agent Competition. It is also designed to support the needs of a group of researchers, each of whom is interested in different decision problems related to the competition scenario. The design of MinneTAC breaks out each basic behavior into a separate, configurable component, and allows dynamic construction of analysis and modeling tools from small, single-purpose "evaluators". The agent is defined as a set of "roles", and a working agent is one for which a component is supplied for each role. This allows each researcher to focus on a single problem and work independently, and it allows multiple researchers to tackle the same problem in different ways. A working MinneTAC agent is completely defined by a set of configuration files that map the desired roles to the code that implements them, and that set parameters for the components. We describe the design of MinneTAC, and we evaluate its effectiveness in support of our research agenda and its competitiveness in the TAC-SCM game environment.