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Abstract: Boosting has attracted much research attention in the past decade. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into acc... |
Title: Least Squares estimation of two ordered monotone regression curves |
Abstract: In this paper, we consider the problem of finding the Least Squares estimators of two isotonic regression curves $g^\circ_1$ and $g^\circ_2$ under the additional constraint that they are ordered; e.g., $g^\circ_1 \le g^\circ_2$. Given two sets of $n$ data points $y_1, ..., y_n$ and $z_1, >...,z_n$ observed at... |
Title: A Distributed Software Architecture for Collaborative Teleoperation based on a VR Platform and Web Application Interoperability |
Abstract: Augmented Reality and Virtual Reality can provide to a Human Operator (HO) a real help to complete complex tasks, such as robot teleoperation and cooperative teleassistance. Using appropriate augmentations, the HO can interact faster, safer and easier with the remote real world. In this paper, we present an e... |
Title: A vanilla Rao--Blackwellization of Metropolis--Hastings algorithms |
Abstract: Casella and Robert [Biometrika 83 (1996) 81--94] presented a general Rao--Blackwellization principle for accept-reject and Metropolis--Hastings schemes that leads to significant decreases in the variance of the resulting estimators, but at a high cost in computation and storage. Adopting a completely differen... |
Title: Inferring Dynamic Bayesian Networks using Frequent Episode Mining |
Abstract: Motivation: Several different threads of research have been proposed for modeling and mining temporal data. On the one hand, approaches such as dynamic Bayesian networks (DBNs) provide a formal probabilistic basis to model relationships between time-indexed random variables but these models are intractable to... |
Title: Delayed rejection schemes for efficient Markov-Chain Monte-Carlo sampling of multimodal distributions |
Abstract: A number of problems in a variety of fields are characterised by target distributions with a multimodal structure in which the presence of several isolated local maxima dramatically reduces the efficiency of Markov Chain Monte Carlo sampling algorithms. Several solutions, such as simulated tempering or the us... |
Title: Uncovering shared common genetic risk factors for various aspects of complex disorders captured in multiple traits |
Abstract: Identifying shared genetic risk factors for multiple measured traits has been of great interest in studying complex disorders. Marlow's (2003) method for detecting shared gene effects on complex traits has been highly influential in the literature of neurodevelopmental disorders as well as other disorders inc... |
Title: Why Global Performance is a Poor Metric for Verifying Convergence of Multi-agent Learning |
Abstract: Experimental verification has been the method of choice for verifying the stability of a multi-agent reinforcement learning (MARL) algorithm as the number of agents grows and theoretical analysis becomes prohibitively complex. For cooperative agents, where the ultimate goal is to optimize some global metric, ... |
Title: Computation of confidence intervals in regression utilizing uncertain prior information |
Abstract: We consider a linear regression model with regression parameter beta =(beta_1, ..., beta_p) and independent and identically N(0, sigma^2)distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau = c^T beta - t where the vector c and ... |
Title: A Methodology for Learning Players' Styles from Game Records |
Abstract: We describe a preliminary investigation into learning a Chess player's style from game records. The method is based on attempting to learn features of a player's individual evaluation function using the method of temporal differences, with the aid of a conventional Chess engine architecture. Some encouraging ... |
Title: Exponential Family Graph Matching and Ranking |
Abstract: We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a posteriori estimation in exponential families with sufficient statistics that encode permutations and data features. Although inference is in general hard, we show that for one very... |
Title: Principle of development |
Abstract: Today, science have a powerful tool for the description of reality - the numbers. However, the concept of number was not immediately, lets try to trace the evolution of the concept. The numbers emerged as the need for accurate estimates of the amount in order to permit a comparison of some objects. So if you ... |
Title: Sparse Bayesian Hierarchical Modeling of High-dimensional Clustering Problems |
Abstract: Clustering is one of the most widely used procedures in the analysis of microarray data, for example with the goal of discovering cancer subtypes based on observed heterogeneity of genetic marks between different tissues. It is well-known that in such high-dimensional settings, the existence of many noise var... |
Title: L1-Penalized Quantile Regression in High-Dimensional Sparse Models |
Abstract: We consider median regression and, more generally, a possibly infinite collection of quantile regressions in high-dimensional sparse models. In these models the overall number of regressors $p$ is very large, possibly larger than the sample size $n$, but only $s$ of these regressors have non-zero impact on th... |
Title: Towards an Intelligent System for Risk Prevention and Management |
Abstract: Making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information, their uncertainties and the unawareness of planners about the future evolution of incidents. The use of a decision support system is an efficient solution of this issue. Such a system can help emerge... |
Title: Agent-Based Decision Support System to Prevent and Manage Risk Situations |
Abstract: The topic of risk prevention and emergency response has become a key social and political concern. One approach to address this challenge is to develop Decision Support Systems (DSS) that can help emergency planners and responders to detect emergencies, as well as to suggest possible course of actions to deal... |
Title: Posterior Inference in Curved Exponential Families under Increasing Dimensions |
Abstract: This work studies the large sample properties of the posterior-based inference in the curved exponential family under increasing dimension. The curved structure arises from the imposition of various restrictions on the model, such as moment restrictions, and plays a fundamental role in econometrics and others... |
Title: Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method |
Abstract: Neighborhood graphs are gaining popularity as a concise data representation in machine learning. However, naive graph construction by pairwise distance calculation takes $O(n^2)$ runtime for $n$ data points and this is prohibitively slow for millions of data points. For strings of equal length, the multiple s... |
Title: FastLMFI: An Efficient Approach for Local Maximal Patterns Propagation and Maximal Patterns Superset Checking |
Abstract: Maximal frequent patterns superset checking plays an important role in the efficient mining of complete Maximal Frequent Itemsets (MFI) and maximal search space pruning. In this paper we present a new indexing approach, FastLMFI for local maximal frequent patterns (itemset) propagation and maximal patterns su... |
Title: HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach |
Abstract: In this paper we present a novel hybrid (arraybased layout and vertical bitmap layout) database representation approach for mining complete Maximal Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techn... |
Title: Ramp: Fast Frequent Itemset Mining with Efficient Bit-Vector Projection Technique |
Abstract: Mining frequent itemset using bit-vector representation approach is very efficient for dense type datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense ... |
Title: Fast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support |
Abstract: Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support thres... |
Title: Using Association Rules for Better Treatment of Missing Values |
Abstract: The quality of training data for knowledge discovery in databases (KDD) and data mining depends upon many factors, but handling missing values is considered to be a crucial factor in overall data quality. Today real world datasets contains missing values due to human, operational error, hardware malfunctionin... |
Title: Introducing Partial Matching Approach in Association Rules for Better Treatment of Missing Values |
Abstract: Handling missing values in training datasets for constructing learning models or extracting useful information is considered to be an important research task in data mining and knowledge discovery in databases. In recent years, lot of techniques are proposed for imputing missing values by considering attribut... |
Title: Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs - Extended Version |
Abstract: In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimistic initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a conventional way, and always follows a greedy policy with respect to its model. ... |
Title: A method for Hedging in continuous time |
Abstract: We present a method for hedging in continuous time. |
Title: Toggling operators in computability logic |
Abstract: Computability logic (CL) (see http://www.cis.upenn.edu/ giorgi/cl.html ) is a research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for interactive computational problems, seen as games b... |
Title: Structured Variable Selection with Sparsity-Inducing Norms |
Abstract: We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsity-inducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual $\ell_1$-norm and the group $\ell_1$-norm by allowing the subsets to ... |
Title: Variations of the Turing Test in the Age of Internet and Virtual Reality |
Abstract: Inspired by Hofstadter's Coffee-House Conversation (1982) and by the science fiction short story SAM by Schattschneider (1988), we propose and discuss criteria for non-mechanical intelligence. Firstly, we emphasize the practical need for such tests in view of massively multiuser online role-playing games (MMO... |
Title: Introduction to Machine Learning: Class Notes 67577 |
Abstract: Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem). |
Title: Considerations upon the Machine Learning Technologies |
Abstract: Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to "learn". Some systems based on Machine... |
Title: Semantic Social Network Analysis |
Abstract: Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have been applied to such ... |
Title: Orbit-Product Representation and Correction of Gaussian Belief Propagation |
Abstract: We present a new view of Gaussian belief propagation (GaBP) based on a representation of the determinant as a product over orbits of a graph. We show that the GaBP determinant estimate captures totally backtracking orbits of the graph and consider how to correct this estimate. We show that the missing orbits ... |
Title: Automated Epilepsy Diagnosis Using Interictal Scalp EEG |
Abstract: Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recogn... |
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