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Do you have any additional comments or suggestions regarding SAS documentation in general that will help us better serve you? How satisfied are you with SAS documentation? How satisfied are you with SAS documentation overall? Do you have any additional comments or suggestions regarding SAS documentation in general that will help us better serve you? Awards In honor of its 25th anniversary, the Machine Learning Journal is sponsoring the awards for the student authors of the best and distinguished papers.
This year, we are recognizing the seminal paper of CRFs. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. 100 and pick them up during the conference at the registration office, aka the Grand Ballroom Coat Check. Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases real-world data lives on a low-dimensional manifold, which should be taken into account to capture meaningful nearest neighbors.
The grouping of features is highly beneficial in learning with high-dimensional data. It reduces the variance in the estimation and improves the stability of feature selection, leading to improved generalization. Moreover, it can also help in data understanding and interpretation. However, its optimization is computationally expensive. However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree-structured hierarchies. In this paper, we present a novel hierarchical multi-label classification algorithm which can be used on both tree- and DAG-structured hierarchies.
Existing multi-task learning or multi-view learning algorithms only capture one type of heterogeneity. Low-rank and sparse structures have been profoundly studied in matrix completion and compressed sensing. We consider multiarmed bandit problems where the expected reward is unimodal over partially ordered arms. In particular, the arms may belong to a continuous interval or correspond to vertices in a graph, where the graph structure represents similarity in rewards. The unimodality assumption has an important advantage: we can determine if a given arm is optimal by sampling the possible directions around it. We propose a method to learn simultaneously a vector-valued function and a kernel between its components. The obtained kernel can be used both to improve learning performances and to reveal structures in the output space which may be important in their own right.
Y, between a structured input space X and a structured output space Y, from labeled and unlabeled examples. Y has a Hilbert space structure. Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of model parameters, which is substantially easier to solve than discrete optimization of cluster assignments.
Portfolio allocation theory has been heavily influenced by a major contribution of Harry Markowitz in the early fifties: the mean-variance approach. While there has been a continuous line of works in on-line learning portfolios over the past decades, very few works have really tried to cope with Markowitz model. A major drawback of the mean-variance approach is that it is approximation-free only when stock returns obey a Gaussian distribution, an assumption known not to hold in real data. In many machine learning applications, labeling every instance of data is burdensome. Though much progress has been made in analyzing MIL problems, existing work considers bags that have a finite number of instances. In recent years, some spectral feature selection methods are proposed to choose those features with high power of preserving sample similarity.