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Roger Chen

Datawocky: How Google Measures Search Quality - 0 views

anand.typepad.com/...e-measures-search-quality.html

data mining google machine learning search

shared by Roger Chen on 11 Jun 08 - Cached
  • The heart of the matter is this: how do you measure the quality of search results
  • The first is that we have all been trained to trust Google and click on the first result no matter what. So ranking models that make slight changes in ranking may not produce significant swings in the measured usage data. The second, more interesting, factor is that users don't know what they're missing.
  • here's the shocker -- these metrics are not very sensitive to new ranking models! When Google tries new ranking models, these metrics sometimes move, sometimes not, and never by much
  • ...1 more annotation...
  • Two learnings from this story: one, the results depend quite strongly on the test set, which again speaks against machine-learned models. And two, Yahoo and Google users differ quite significantly in the kinds of searches they do
Roger Chen

Datawocky: Are Machine-Learned Models Prone to Catastrophic Errors? - 0 views

anand.typepad.com/...an-machine-learned-models.html

data mining google

shared by Roger Chen on 11 Jun 08 - Cached
  • Taleb makes a convincing case that most real-world phenomena we care about actually inhabit Extremistan rather than Mediocristan. In these cases, you can make quite a fool of yourself by assuming that the future looks like the past.
  • The current generation of machine learning algorithms can work well in Mediocristan but not in Extremistan.
  • It has long been known that Google's search algorithm actually works at 2 levels: An offline phase that extracts "signals" from a massive web crawl and usage data. An example of such a signal is page rank. These computations need to be done offline because they analyze massive amounts of data and are time-consuming. Because these signals are extracted offline, and not in response to user queries, these signals are necessarily query-independent. You can think of them tags on the documents in the index. There are about 200 such signals. An online phase, in response to a user query. A subset of documents is identified based on the presence of the user's keywords. Then, these documents are ranked by a very fast algorithm that combines the 200 signals in-memory using a proprietary formula.
  • ...2 more annotations...
  • This raises a fundamental philosophical question. If Google is unwilling to trust machine-learned models for ranking search results, can we ever trust such models for more critical things, such as flying an airplane, driving a car, or algorithmic stock market trading? All machine learning models assume that the situations they encounter in use will be similar to their training data. This, however, exposes them to the well-known problem of induction in logic.
  • My hunch is that humans have evolved to use decision-making methods that are less likely blow up on unforeseen events (although not always, as the mortgage crisis shows)
Roger Chen

Data & Knowledge Engineering (0169-023X) - ACM Guide to Computing Literature - 0 views

portal.acm.org/toc.cfm

data mining journals papers

shared by Roger Chen on 30 Jun 08 - Cached
  • Roger Chen on 30 Jun 08
     
    Data & Knowledge Engineering (0169-023X)
Roger Chen

Expert Systems with Applications: An International Journal (0957-4174) - 0 views

portal.acm.org/toc.cfm

data mining journals papers

shared by Roger Chen on 30 Jun 08 - Cached
  • Roger Chen on 30 Jun 08
     
    ACM Portal - Expert Systems with Applications: An International Journal
Roger Chen

Data Randomization - 0 views

research.microsoft.com/...view.aspx

data mining papers reference research

shared by Roger Chen on 04 Sep 08 - Cached
  • Roger Chen on 04 Sep 08
     
    Attacks that exploit memory errors are still a serious problem. We present data randomization, a new technique that provides probabilistic protection against these attacks by xoring data with random masks. Data randomization uses static analysis to partition instruction operands into equivalence classes: it places two operands in the same class if they may refer to the same object in an execution that does not violate memory safety. Then it assigns a random mask to each class and it generates code instrumented to xor data read from or written to memory with the mask of the memory operand's class. Therefore, attacks that violate the results of the static analysis have unpredictable results. We implemented a data randomization prototype that compiles programs without modifications and can preventmany attacks with low overhead. Our prototype prevents all the attacks in our benchmarks while introducing an average runtime overhead of 11% (0%to 27%) and an average space overhead below 1%.
Roger Chen

Attribute-Relation File Format (ARFF) - 0 views

www.cs.waikato.ac.nz/...arff.html

data mining python reference

shared by Roger Chen on 30 Jul 08 - Cached
  • Roger Chen on 30 Jul 08
     
    An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes. ARFF files were developed by the Machine Learning Project at the Department of Computer Science of The University of Waikato for use with the Weka machine learning software.
Roger Chen

KNIME - Konstanz Information Miner - 0 views

www.knime.org

data mining tools

shared by Roger Chen on 01 Aug 08 - Cached
  • Roger Chen on 01 Aug 08
     
    KNIME, pronounced [naim], is a modular data exploration platform that enables the user to visually create data flows (often referred to as pipelines), selectively execute some or all analysis steps, and later investigate the results through interactive views on data and models.
Roger Chen

GroupLens Research - 0 views

www.grouplens.org

academia people data mining recommender

shared by Roger Chen on 26 Jun 08 - Cached
  • Roger Chen on 26 Jun 08
     
    GroupLens is a research lab in the Department of Computer Science and Engineering at the University of Minnesota. We conduct research in several areas, including: * recommender systems * online communities * mobile and ubiquitious technologies * digital libraries * local geographic information systems
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