Generalized Gauss Inequalities via Semidefinite Programming
... This result is powerful because the SDP reformulation of the worst-case probability problem is exact and can be solved in polynomial time using modern interior point methods [41]. Moreover, the equivalent SDP can conveniently be embedded into higher-level optimization problems such as distributional ...
... This result is powerful because the SDP reformulation of the worst-case probability problem is exact and can be solved in polynomial time using modern interior point methods [41]. Moreover, the equivalent SDP can conveniently be embedded into higher-level optimization problems such as distributional ...
Semi-Lazy Learning: Combining Clustering and Classifiers to Build
... will demonstrate. We show that building local models allows for improved results over just building a single model using an eager learner or using K-Nearest neighbor learners. We can obtain better results without the penalty of storing and searching the entire training set for every test set instanc ...
... will demonstrate. We show that building local models allows for improved results over just building a single model using an eager learner or using K-Nearest neighbor learners. We can obtain better results without the penalty of storing and searching the entire training set for every test set instanc ...
Data Mining as Support to Knowledge Management in Marketing
... the same, and observes the change of the model error. The sensitivity coefficient of each input variable presents the ratio of the average model error with changes of an examined input variable in relation to model error without changes of an examined input variable. The variable whose sensitivity r ...
... the same, and observes the change of the model error. The sensitivity coefficient of each input variable presents the ratio of the average model error with changes of an examined input variable in relation to model error without changes of an examined input variable. The variable whose sensitivity r ...
Learning with Hierarchical-Deep Models
... performance by learning parameters at all levels jointly by maximizing a joint log-probability score. There have also been several approaches in the computer vision community addressing the problem of learning with few examples. Torralba et al. [42] proposed using several boosted detectors in a mult ...
... performance by learning parameters at all levels jointly by maximizing a joint log-probability score. There have also been several approaches in the computer vision community addressing the problem of learning with few examples. Torralba et al. [42] proposed using several boosted detectors in a mult ...
PDF only - at www.arxiv.org.
... contain a variable that is subject to change. To describe a dynamic learning process we need a calculus, and Cox (1946) laid the groundwork for a demonstration (Terenin and Draper, 2015) that the only scalar calculus that is consistent with the classical logic is probability theory, at least under c ...
... contain a variable that is subject to change. To describe a dynamic learning process we need a calculus, and Cox (1946) laid the groundwork for a demonstration (Terenin and Draper, 2015) that the only scalar calculus that is consistent with the classical logic is probability theory, at least under c ...
Simulation of Fuzzy Multiattribute Models
... another approach reflecting uncertainty into the basic multiattribute model. This paper discusses the use of a common data analysis technique, i.e, Monte Carlo Simulation, to this model to ...
... another approach reflecting uncertainty into the basic multiattribute model. This paper discusses the use of a common data analysis technique, i.e, Monte Carlo Simulation, to this model to ...
Variational Inference for Dirichlet Process Mixtures
... In the DP mixture, the vector π(v) comprises the infinite vector of mixing proportions and {η1∗ , η2∗ , . . .} are the atoms representing the mixture components. Let Zn be an assignment variable of the mixture component with which the data point x n is associated. The data can be described as arisin ...
... In the DP mixture, the vector π(v) comprises the infinite vector of mixing proportions and {η1∗ , η2∗ , . . .} are the atoms representing the mixture components. Let Zn be an assignment variable of the mixture component with which the data point x n is associated. The data can be described as arisin ...
Document
... – “EDA is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there , as well as those we believe will be there” (John Tukey) ...
... – “EDA is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there , as well as those we believe will be there” (John Tukey) ...
The potential existence of chaotic behavior in financial markets has
... behavior: conditional heteroskedasticity. In my analysis, I set out by embedding the data in two and three dimensions using the time delay method. For the original (detrended) data I use a time delay of 400 days, while for the log-ed data I use a time delay of 500, since in this way the shape of the ...
... behavior: conditional heteroskedasticity. In my analysis, I set out by embedding the data in two and three dimensions using the time delay method. For the original (detrended) data I use a time delay of 400 days, while for the log-ed data I use a time delay of 500, since in this way the shape of the ...
Efficient Bayesian estimates for discrimination
... A major effort in systems biology is the development of mathematical models that describe complex biological systems at multiple scales and levels of abstraction. Determining the topology—the set of interactions—of a biological system from observations of the system’s behavior is an important and diffi ...
... A major effort in systems biology is the development of mathematical models that describe complex biological systems at multiple scales and levels of abstraction. Determining the topology—the set of interactions—of a biological system from observations of the system’s behavior is an important and diffi ...
Radial Basis Functions: An Algebraic Approach (with Data Mining
... • Formally, we are given data set D = {( x i , y i ) : x i ∈ R d , y i , i = 1,..., n} ...
... • Formally, we are given data set D = {( x i , y i ) : x i ∈ R d , y i , i = 1,..., n} ...
An Introduction to Probabilistic Graphical Models.
... A graphical model can be thought of as a probabilistic database, a machine that can answer “queries” regarding the values of sets of random variables. ...
... A graphical model can be thought of as a probabilistic database, a machine that can answer “queries” regarding the values of sets of random variables. ...