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... What is Cluster Analysis? • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Intra-cluster distances are ...
... What is Cluster Analysis? • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Intra-cluster distances are ...
Discovering Interesting Patterns Through User`s Interactive Feedback
... the items, and xj is chosen from {ij , ij }. For example, u(i1 , i2 ) denotes the interaction between i1 and i2 . The saturated log-linear model depicted above is the most general model for n items. Simpler log-linear model reduces the number of parameters by specifying certain interaction effects t ...
... the items, and xj is chosen from {ij , ij }. For example, u(i1 , i2 ) denotes the interaction between i1 and i2 . The saturated log-linear model depicted above is the most general model for n items. Simpler log-linear model reduces the number of parameters by specifying certain interaction effects t ...
Contrast Data Mining: Methods and Applications
... Contrast pattern based classification and improvement of traditional methods ...
... Contrast pattern based classification and improvement of traditional methods ...
R u t c o r Research A New Approach to Select
... specific condition (e.g., patients with specific disease) are called positive observations, and the vectors in D I − corresponding to those observations that do not have the condition (e.g., patients not having the disease) are called negative observations. The components of the vectors, called “fea ...
... specific condition (e.g., patients with specific disease) are called positive observations, and the vectors in D I − corresponding to those observations that do not have the condition (e.g., patients not having the disease) are called negative observations. The components of the vectors, called “fea ...
Modeling Toothpaste Brand Choice
... behavior turned their focus on measurable parameters like prices, purchase frequency, and average purchase size. Consequently, the effort of using behavioral data towards developing decision tools for planning marketing activities have resulted in numerous different ...
... behavior turned their focus on measurable parameters like prices, purchase frequency, and average purchase size. Consequently, the effort of using behavioral data towards developing decision tools for planning marketing activities have resulted in numerous different ...
TRACTABLE AND CONSISTENT RANDOM GRAPH MODELS
... ...[A] pertinent form of statistical treatment would be one which deals with social configurations as wholes, and not with single series of facts, more or less artificially separated from the total picture. Jacob Levy Moreno and Helen Hall Jennings, 1938. For a researcher interested in an economic o ...
... ...[A] pertinent form of statistical treatment would be one which deals with social configurations as wholes, and not with single series of facts, more or less artificially separated from the total picture. Jacob Levy Moreno and Helen Hall Jennings, 1938. For a researcher interested in an economic o ...
PowerPoint
... model structures with varying sets of objects • Intuition: Stochastic generative process with two kinds of steps: – Set the value of a function on a tuple of arguments – Add some number of objects to the world ...
... model structures with varying sets of objects • Intuition: Stochastic generative process with two kinds of steps: – Set the value of a function on a tuple of arguments – Add some number of objects to the world ...
Bayesian Classification, Nearest Neighbors, Ensemble Methods
... risk of overfitting due to noise in the training data Value of k can be chosen based on error rate measures We should also avoid over-smoothing by choosing k=n, where n is the total number of tuples in the training data set ...
... risk of overfitting due to noise in the training data Value of k can be chosen based on error rate measures We should also avoid over-smoothing by choosing k=n, where n is the total number of tuples in the training data set ...
A Probabilistic Approach to Spatiotemporal Theme Pattern Mining
... words from a vocabulary set V = {w1 , ..., w|V | }. In information retrieval and text mining, it is quite common to use a word distribution to model topics, subtopics, or themes in text[3, 12, 1, 21]. Following [21], we define a theme as follows: Definition 1 (Theme) A theme in a text collection C i ...
... words from a vocabulary set V = {w1 , ..., w|V | }. In information retrieval and text mining, it is quite common to use a word distribution to model topics, subtopics, or themes in text[3, 12, 1, 21]. Following [21], we define a theme as follows: Definition 1 (Theme) A theme in a text collection C i ...
A Context-aware Time Model for Web Search
... To account for the context bias effect, we propose a contextaware time model (CATM) that allows us to predict probability distributions of times between two user actions in a given context (which we represent by a sequence of previous user interactions with the search engine). The CATM can be used(i ...
... To account for the context bias effect, we propose a contextaware time model (CATM) that allows us to predict probability distributions of times between two user actions in a given context (which we represent by a sequence of previous user interactions with the search engine). The CATM can be used(i ...
Probabilistic models for spike trains of single neurons
... The primary mode of information transmission in neural networks is unknown: is it a rate code or a timing code? Assuming that presynaptic spike trains are stochastic and a rate code is used, probabilistic models of spiking can reveal properties of the neural computation performed at the level of sin ...
... The primary mode of information transmission in neural networks is unknown: is it a rate code or a timing code? Assuming that presynaptic spike trains are stochastic and a rate code is used, probabilistic models of spiking can reveal properties of the neural computation performed at the level of sin ...
as a PDF - Idiap Publications
... Speech signal conveys different information such as the pronounced words or the speaker characteristics. It is very difficult to separate these information. Thus, speaker verification systems are generally classified following their degree of dependence on the pronounced text: text-dependent, text-p ...
... Speech signal conveys different information such as the pronounced words or the speaker characteristics. It is very difficult to separate these information. Thus, speaker verification systems are generally classified following their degree of dependence on the pronounced text: text-dependent, text-p ...
A New Measure for the Accuracy of a Bayesian Network
... The range of values for the degree of accuracy of a Bayesian network, with respect to any data set, is (-∞,0]. Since the MDL formalism evaluates the likelihood of a Bayesian network given a particular data set, the specific range of values for the degree of accuracy of a Bayesian network, with respe ...
... The range of values for the degree of accuracy of a Bayesian network, with respect to any data set, is (-∞,0]. Since the MDL formalism evaluates the likelihood of a Bayesian network given a particular data set, the specific range of values for the degree of accuracy of a Bayesian network, with respe ...
Improving and extending the testing of distributions
... Definition 2.3 (Conditional oracle). A conditional oracle to a distribution µ supportedP over [n] is a black-box that takes as input a set A ⊆ [n], samples a point i ∈ A with probability µ(i)/ j∈A µ(j), and returns i. If µ(j) = 0 for all j ∈ A, then it chooses i ∈ A uniformly at random. Remark. The ...
... Definition 2.3 (Conditional oracle). A conditional oracle to a distribution µ supportedP over [n] is a black-box that takes as input a set A ⊆ [n], samples a point i ∈ A with probability µ(i)/ j∈A µ(j), and returns i. If µ(j) = 0 for all j ∈ A, then it chooses i ∈ A uniformly at random. Remark. The ...