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Markov logic networks
TLDR
Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation.
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Mining high-speed data streams
TLDR
This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and constant time per example, and applies it to mining the continuous stream of Web access data from the whole University of Washington main campus.
View on ACM
Mining the network value of customers
TLDR
It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
TLDR
The Bayesian classifier is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption, and will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain.
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Sum-product networks: A new deep architecture
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are the most general conditions under which the partition…
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Mining time-changing data streams
TLDR
An efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner is proposed, called CVFDT, which stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate.
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MetaCost: a general method for making classifiers cost-sensitive
TLDR
A principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it is proposed, called MetaCost, which treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
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A few useful things to know about machine learning
TLDR
Tapping into the "folk knowledge" needed to advance machine learning applications is a natural next step in the development of artificial intelligence systems.
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Mining knowledge-sharing sites for viral marketing
TLDR
This research optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him, and takes into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost.
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Markov Logic: An Interface Layer for Artificial Intelligence
TLDR
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success, but this interface layer has been missing in AI.
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