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Title: Nonparametric estimation of a trend based upon sampled continuous processes |
Abstract: Let X be a second order random process indexed by a compact interval [0,T]. Assume that n independent realizations of X are observed on a fixed grid of p time points. Under mild regularity assumptions on the sample paths of X, we show the asymptotic normality of suitable nonparametric estimators of the trend ... |
Title: Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts |
Abstract: Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in ... |
Title: Sparse Component Analysis (SCA) in Random-valued and Salt and Pepper Noise Removal |
Abstract: In this paper, we propose a new method for impulse noise removal from images. It uses the sparsity of images in the Discrete Cosine Transform (DCT) domain. The zeros in this domain give us the exact mathematical equation to reconstruct the pixels that are corrupted by random-value impulse noises. The proposed... |
Title: New parallel programming language design: a bridge between brain models and multi-core/many-core computers? |
Abstract: The recurrent theme of this paper is that sequences of long temporal patterns as opposed to sequences of simple statements are to be fed into computation devices, being them (new proposed) models for brain activity or multi-core/many-core computers. In such models, parts of these long temporal patterns are al... |
Title: A Growing Self-Organizing Network for Reconstructing Curves and Surfaces |
Abstract: Self-organizing networks such as Neural Gas, Growing Neural Gas and many others have been adopted in actual applications for both dimensionality reduction and manifold learning. Typically, in these applications, the structure of the adapted network yields a good estimate of the topology of the unknown subspac... |
Title: Analyse et structuration automatique des guides de bonnes pratiques cliniques : essai d'\'evaluation |
Abstract: Health Practice Guideliens are supposed to unify practices and propose recommendations to physicians. This paper describes GemFrame, a system capable of semi-automatically filling an XML template from free texts in the clinical domain. The XML template includes semantic information not explicitly encoded in t... |
Title: Recent Developments in Nonregular Fractional Factorial Designs |
Abstract: Nonregular fractional factorial designs such as Plackett-Burman designs and other orthogonal arrays are widely used in various screening experiments for their run size economy and flexibility. The traditional analysis focuses on main effects only. Hamada and Wu (1992) went beyond the traditional approach and ... |
Title: A Computational Model to Disentangle Semantic Information Embedded in Word Association Norms |
Abstract: Two well-known databases of semantic relationships between pairs of words used in psycholinguistics, feature-based and association-based, are studied as complex networks. We propose an algorithm to disentangle feature based relationships from free association semantic networks. The algorithm uses the rich top... |
Title: Binary Classification Based on Potentials |
Abstract: We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector Machine methods. |
Title: Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines |
Abstract: We investigate the performance of a simple signed distance function (SDF) based method by direct comparison with standard SVM packages, as well as K-nearest neighbor and RBFN methods. We present experimental results comparing the SDF approach with other classifiers on both synthetic geometric problems and fiv... |
Title: Ultrahigh dimensional variable selection: beyond the linear model |
Abstract: Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Many frequently-used techniques are based on independence screening; examples include correlation ranking (Fan and Lv, 2008) or feature selection using a two-sample t-test in high... |
Title: BiopSym: a simulator for enhanced learning of ultrasound-guided prostate biopsy |
Abstract: This paper describes a simulator of ultrasound-guided prostate biopsies for cancer diagnosis. When performing biopsy series, the clinician has to move the ultrasound probe and to mentally integrate the real-time bi-dimensional images into a three-dimensional (3D) representation of the anatomical environment. ... |
Title: Quantum Predictive Learning and Communication Complexity with Single Input |
Abstract: We define a new model of quantum learning that we call Predictive Quantum (PQ). This is a quantum analogue of PAC, where during the testing phase the student is only required to answer a polynomial number of testing queries. We demonstrate a relational concept class that is efficiently learnable in PQ, while ... |
Title: Linearly Parameterized Bandits |
Abstract: We consider bandit problems involving a large (possibly infinite) collection of arms, in which the expected reward of each arm is a linear function of an $r$-dimensional random vector $ \in ^r$, where $r \geq 2$. The objective is to minimize the cumulative regret and Bayes risk. When the set of arms correspon... |
Title: Automatic Construction of Lightweight Domain Ontologies for Chemical Engineering Risk Management |
Abstract: The need for domain ontologies in mission critical applications such as risk management and hazard identification is becoming more and more pressing. Most research on ontology learning conducted in the academia remains unrealistic for real-world applications. One of the main problems is the dependence on non-... |
Title: Bias correction in a multivariate normal regression model with general parameterization |
Abstract: This paper develops a bias correction scheme for a multivariate normal model under a general parameterization. In the model, the mean vector and the covariance matrix share the same parameters. It includes many important regression models available in the literature as special cases, such as (non)linear regre... |
Title: A New Method for Knowledge Representation in Expert System's (XMLKR) |
Abstract: Knowledge representation it is an essential section of a Expert Systems, Because in this section we have a framework to establish an expert system then we can modeling and use by this to design an expert system. Many method it is exist for knowledge representation but each method have problems, in this paper ... |
Title: Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer |
Abstract: In this paper, we propose a new method remMap -- REgularized Multivariate regression for identifying MAster Predictors -- for fitting multivariate response regression models under the high-dimension-low-sample-size setting. remMap is motivated by investigating the regulatory relationships among different biol... |
Title: A New Family of Covariate-Adjusted Response Adaptive Designs and their Asymptotic Properties |
Abstract: It is often important to incorporating covariate information in the design of clinical trials. In literature, there are many designs of using stratification and covariate-adaptive randomization to balance on certain known covariate. Recently Zhang, Hu, Cheung and Chan (2007) have proposed a family of covariat... |
Title: Immigrated urn models - asymptotic properties and applications |
Abstract: Urn models have been widely studied and applied in both scientific and social science disciplines. In clinical studies, the adoption of urn models in treatment allocation schemes has been proved to be beneficial to both researchers, by providing more efficient clinical trials, and patients, by increasing the ... |
Title: Multi-color Randomly Reinforced Urn for Adaptive Designs |
Abstract: This paper is withdrawn |
Title: The Offset Tree for Learning with Partial Labels |
Abstract: We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather than all choices. The algorithm reduces this setting to binary classification, allowing one to reuse of any existing, fully supervised binary classification alg... |
Title: Finding Still Lifes with Memetic/Exact Hybrid Algorithms |
Abstract: The maximum density still life problem (MDSLP) is a hard constraint optimization problem based on Conway's game of life. It is a prime example of weighted constrained optimization problem that has been recently tackled in the constraint-programming community. Bucket elimination (BE) is a complete technique co... |
Title: Client-server multi-task learning from distributed datasets |
Abstract: A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multi... |
Title: Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes |
Abstract: I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-ra... |
Title: The Latent Relation Mapping Engine: Algorithm and Experiments |
Abstract: Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for compl... |
Title: Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems |
Abstract: The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the existence of central authorities superfluous and even impossible. At the same time, recommender systems are gaining considerable impac... |
Title: Feature Markov Decision Processes |
Abstract: General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the righ... |
Title: Feature Dynamic Bayesian Networks |
Abstract: Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I ext... |
Title: I, Quantum Robot: Quantum Mind control on a Quantum Computer |
Abstract: The logic which describes quantum robots is not orthodox quantum logic, but a deductive calculus which reproduces the quantum tasks (computational processes, and actions) taking into account quantum superposition and quantum entanglement. A way toward the realization of intelligent quantum robots is to adopt ... |
Title: Identifying Relevant Eigenimages - a Random Matrix Approach |
Abstract: Dimensional reduction of high dimensional data can be achieved by keeping only the relevant eigenmodes after principal component analysis. However, differentiating relevant eigenmodes from the random noise eigenmodes is problematic. A new method based on the random matrix theory and a statistical goodness-of-... |
Title: A hierarchical Dirichlet process mixture model for haplotype reconstruction from multi-population data |
Abstract: The perennial problem of "how many clusters?" remains an issue of substantial interest in data mining and machine learning communities, and becomes particularly salient in large data sets such as populational genomic data where the number of clusters needs to be relatively large and open-ended. This problem g... |
Title: Single-Index Model-Assisted Estimation In Survey Sampling |
Abstract: A model-assisted semiparametric method of estimating finite population totals is investigated to improve the precision of survey estimators by incorporating multivariate auxiliary information. The proposed superpopulation model is a single-index model which has proven to be a simple and efficient semiparametr... |
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