Combining Classifiers with Meta Decision Trees
... obtain the final prediction from the predictions of the base-level classifiers. Cascading is an iterative process of combining classifiers: at each iteration, the training data set is extended with the predictions obtained in the previous iteration. The work presented here focuses on combining the p ...
... obtain the final prediction from the predictions of the base-level classifiers. Cascading is an iterative process of combining classifiers: at each iteration, the training data set is extended with the predictions obtained in the previous iteration. The work presented here focuses on combining the p ...
Visually–driven analysis of movement data by progressive clustering
... partition-based, hierarchical, and density-based algorithms (Kaufman & Rousseeuw, 1990). The partition-based methods, as suggested by the name, partition the dataset into sub-groups, such that the objective function for each cluster is maximized. In order to reach a good approximation, the partition ...
... partition-based, hierarchical, and density-based algorithms (Kaufman & Rousseeuw, 1990). The partition-based methods, as suggested by the name, partition the dataset into sub-groups, such that the objective function for each cluster is maximized. In order to reach a good approximation, the partition ...
- City Research Online
... partition-based, hierarchical, and density-based algorithms (Kaufman & Rousseeuw, 1990). The partition-based methods, as suggested by the name, partition the dataset into sub-groups, such that the objective function for each cluster is maximized. In order to reach a good approximation, the partition ...
... partition-based, hierarchical, and density-based algorithms (Kaufman & Rousseeuw, 1990). The partition-based methods, as suggested by the name, partition the dataset into sub-groups, such that the objective function for each cluster is maximized. In order to reach a good approximation, the partition ...
An Explorative Parameter Sweep: Spatial-temporal Data
... model. All these features could then be used to compare different simulation (with different parameter settings) with some proximity analysis (goal 2). An essential question will be; how will these features behave when stochasticity is abundant (signal-to-noise ratio)? There is a risk that feature s ...
... model. All these features could then be used to compare different simulation (with different parameter settings) with some proximity analysis (goal 2). An essential question will be; how will these features behave when stochasticity is abundant (signal-to-noise ratio)? There is a risk that feature s ...
Fast Rank-2 Nonnegative Matrix Factorization for
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
Outlier
... Example (right figure): First use Gaussian distribution to model the normal data For each object y in region R, estimate gD(y), the probability of y fits the Gaussian distribution If gD(y) is very low, y is unlikely generated by the Gaussian model, thus an outlier Effectiveness of statistical me ...
... Example (right figure): First use Gaussian distribution to model the normal data For each object y in region R, estimate gD(y), the probability of y fits the Gaussian distribution If gD(y) is very low, y is unlikely generated by the Gaussian model, thus an outlier Effectiveness of statistical me ...
SFU Thesis Template Files - SFU`s Institutional Repository
... Benchmarking is a process of comparison between performance characteristics of separate, often competing organizations intended to enable each participant to improve its own performance in the marketplace (Kay, 2007). Benchmarking sets organizations’ performance standards based on what “others” are ...
... Benchmarking is a process of comparison between performance characteristics of separate, often competing organizations intended to enable each participant to improve its own performance in the marketplace (Kay, 2007). Benchmarking sets organizations’ performance standards based on what “others” are ...
Quality scheme assessment in the clustering process
... partitioning of the specific data set based on a well defined quality index. In the following sections we elaborate on our approach. 3.1 Quality of Clustering Schemes The objective of the clustering methods is to provide optimal partitions of a data set. In general, they should search for clusters ...
... partitioning of the specific data set based on a well defined quality index. In the following sections we elaborate on our approach. 3.1 Quality of Clustering Schemes The objective of the clustering methods is to provide optimal partitions of a data set. In general, they should search for clusters ...
Machine learning and data mining for yeast functional genomics
... These include algorithms for multi-label learning (where each example belongs to more than one possible class), learning with both hierarchically-structured data and classes, and a distributed first order association mining algorithm for use on a Beowulf cluster. We use bootstrap techniques for samp ...
... These include algorithms for multi-label learning (where each example belongs to more than one possible class), learning with both hierarchically-structured data and classes, and a distributed first order association mining algorithm for use on a Beowulf cluster. We use bootstrap techniques for samp ...
Chapter 6
... Uses MDL-based stopping criterion Employs post-processing step to modify rules guided by MDL criterion ...
... Uses MDL-based stopping criterion Employs post-processing step to modify rules guided by MDL criterion ...
Clustering, Dimensionality Reduction, and Side
... There are so many people who have been so kind and so helpful to me during all these years; all of you have made a mark in my life! First and foremost, I want to express my greatest gratitude to my thesis supervisor Dr. Anil Jain. He is such a wonderful advisor, mentor, and motivator. Under his guid ...
... There are so many people who have been so kind and so helpful to me during all these years; all of you have made a mark in my life! First and foremost, I want to express my greatest gratitude to my thesis supervisor Dr. Anil Jain. He is such a wonderful advisor, mentor, and motivator. Under his guid ...
Clustering of time series data—a survey
... Just like static data clustering, time series clustering requires a clustering algorithm or procedure to form clusters given a set of unlabeled data objects and the choice of clustering algorithm depends both on the type of data available and on the particular purpose and application. As far as time ...
... Just like static data clustering, time series clustering requires a clustering algorithm or procedure to form clusters given a set of unlabeled data objects and the choice of clustering algorithm depends both on the type of data available and on the particular purpose and application. As far as time ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.