11,307 research outputs found
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
Research on Real Time Traffic Information Data Model and Its Data Transmit
Real-time of geographic information system for transportation (GIS-T) is one of the essential conditions to alleviate the traffic jam and guide the traffic flow rationally. In order to make it convenient for sharing and maintaining data, this paper structures the independent real-time traffic information database, seamless merging real-time traffic information and GIS data through data fusion method. In order to realize this purpose, the paper research on baseline network data model, baseline network is composed of base points and baselines. Base points are exclusive locating on the road network, which can be determined in field, and also can be resumed. Baseline is line element, which replaces traffic event, the baseline locate road network by the point, and therefore, it is easy to realize data share for various linear reference system. According to the data model, designing structure and introducing data transmit flow of the Geographic Information System for Transportation. Key words: Data Model; Data Fusion; GIS; Traffic Information This paper is supported by the Department of Instrument Science and Engineering, Southeast University, and professor DE-JUN WAN and professor QING WAN
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