14954871
Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
Abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.
120407
A Hierarchical Multiclassifier System for Hyperspectral Data Analysis
Many real world classification problems involve high dimensional inputs and a large number of classes. Feature extraction and modular learning approaches can be used to simplify such problems. In this paper, we introduce a hierarchical multiclassifier paradigm in which a C- class problem is recursively decomposed into C - 1 two-class problems. A generalized modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problem of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier architecture was used to classify 12 types of landcover from 183-dimensional hyperspectral data. The classification accuracy was significantly improved by 4 to 10% relative to other feature extraction and modular learning approaches. Moreover, the class hierarchy that was automatically discovered conformed very well with a human domain expert's opinion, which demonstrates the potential of such a modular learning approach for discovering domain knowledge automatically from data.
36844407
Performance Bounds of Algorithms for Scheduling Advertisements on a Web Page
AbstractConsider a set of n advertisements (hereafter called “ads”) A ={A1,...,An} competing to be placed in a planning horizon which is divided into N time intervals called slots. An ad Ai is specified by its size si and frequency wi. The size si represents the amount of space the ad occupies in a slot. Ad Ai is said to be scheduled if exactly wi copies of Ai are placed in the slots subject to the restriction that a slot contains at most one copy of an ad. In this paper, we consider two problems. The MINSPACE problem minimizes the maximum fullness among all slots in a feasible schedule where the fullness of a slot is the sum of the sizes of ads assigned to the slot. For the MAXSPACE problem, in addition, we are given a common maximum fullness S for all slots. The total size of the ads placed in a slot cannot exceed S. The objective is to find a feasible schedule 
$$A' \subseteq A$$
 of ads such that the total occupied slot space 
$$\sum {_{A_i \in A'} w_i s_i }$$
 is maximized. We examine the complexity status of both problems and provide heuristics with performance guarantees.
6244213
A Smorgasbord of Features for Statistical Machine Translation
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.
1474217
An observation-constrained generative approach for probabilistic classification of image regions
Abstract In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.
26327552
Increased watermark-to-host correlation of uniform random phase watermarks in audio signals
We analyze the watermark-to-host correlation (WHC) of random phase watermarks, in the context of additive embedding and blind correlation-detection of the watermark in audio signals. Interestingly, we find that WHC is higher when a legitimate watermark is present in the audio signal. Instead of trying to minimize the WHC, one could also attempt to harness this increased correlation in order to result in better detection performance. The analysis shows that uniformly distributed phase difference (between the host signal and the watermark) provides maximum advantage. It is also verified that, a uniformly distributed watermark phase provides the most advantage, experimented over a variety of audio signals.
10689850
Discriminative random fields: a discriminative framework for contextual interaction in classification
In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.