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Aliferis
Constantin Aliferis, New York, NY US
| Patent application number | Description | Published |
|---|---|---|
| 20090077068 | CONTENT AND QUALITY ASSESSMENT METHOD AND APPARATUS FOR QUALITY SEARCHING - A computer-based process retrieves information organized in documents containing text and/or coded representations of text. The process involves obtaining and labeling a selected set of documents based on content quality, and extracting and representing features from each document in the selected set. The extracted and selected features are modified, and models are constructed using parametric learning algorithms. The constructed models are capable of assigning a label to each document. The model parameters are instantiated using a first subset of the selected set of documents. Parameters are chosen by validating the corresponding model against at least a second subset of the full document set. The constructed models also are capable of assigning labels to similar documents outside a selected subset not previously given to the process of model construction. | 03-19-2009 |
| 20090157585 | Method for predicting citation counts - A computerized process to predict citation counts of articles comprising the steps of receiving an article through an input, obtaining, through the input, a selected set of articles exclusive of the article, storing in a memory the set of articles and the article, extracting through a computer processor an article feature from each article in the stored set of articles, constructing models through the computer processor using a pattern recognition process and the article feature, selecting, through the processor, a best model, predicting by application of the best model to the article by the processor a future citation count of the article, outputting, the article comprising the future citation count and controlling through a publication controller unit, distribution of the article. | 06-18-2009 |
Constantin F. Aliferis, Nashville, TN US
| Patent application number | Description | Published |
|---|---|---|
| 20110246403 | Method and System for Automated Supervised Data Analysis - The invention relates to a method for automatically analyzing data and constructing data classification models based on the data. In an embodiment of the method, the method includes selecting a best combination of methods from a plurality of classification, predictor selection, and data preparatory methods; and determining a best model that corresponds to one or more best parameters of the classification, predictor selection, and data preparatory methods for the data to be analyzed. The method also includes estimating the performance of the best model using new data that was not used in selecting the best combination of methods or in determining the best model; and returning a small set of predictors sufficient for the classification task. | 10-06-2011 |
Konstantinos (constantin) F. Aliferis, New York, NY US
| Patent application number | Description | Published |
|---|---|---|
| 20100217599 | Computer Implemented Method for Determining All Markov Boundaries and its Application for Discovering Multiple Maximally Accurate and Non-Redundant Predictive Models - Methods for discovery of a Markov boundary from data constitute one of the most important recent developments in pattern recognition and applied statistics, primarily because they offer a principled solution to the variable/feature selection problem and give insight about local causal structure. Even though there is always a single Markov boundary of the response variable in faithful distributions, distributions with violations of the intersection property of probability theory may have multiple Markov boundaries. Such distributions are abundant in practical data-analytic applications, and there are several reasons why it is important to discover all Markov boundaries from such data. The present invention is a novel computer implemented generative method (termed TIE*) that can discover all Markov boundaries from a data sample drawn from a distribution. TIE* can be instantiated to discover all and only Markov boundaries independent of data distribution. TIE* has been tested with simulated and re-simulated data and then applied to (a) identify the set of maximally accurate and non-redundant molecular signatures and to (b) discover Markov boundaries in datasets from several application domains including but not limited to: biology, medicine, economics, ecology, digit recognition, text categorization, and computational biology. | 08-26-2010 |
| 20100217731 | Computer Implemented Method for the Automatic Classification of Instrumental Citations - The learning method taught in this patent document is significantly different from previous methods for automatic classification of citations that are labor intensive and subject to human bias and error. The present invention automatically generates and avoids these limitations. A set of operational definitions and features uniquely suited to the scientific literature is disclosed along with their use with a learning method that is capable of analyzing the textual content of articles along with bibliometric data to accurately classify instrumental citations. | 08-26-2010 |
| 20110202322 | Computer Implemented Method for Discovery of Markov Boundaries from Datasets with Hidden Variables - Methods for Markov boundary discovery are important recent developments in pattern recognition and applied statistics, primarily because they offer a principled solution to the variable/feature selection problem and give insight about local causal structure. Currently there exist two major local method families for identification of Markov boundaries from data: methods that directly implement the definition of the Markov boundary and newer compositional Markov boundary methods that are more sample efficient and thus often more accurate in practical applications. However, in the datasets with hidden (i.e., unmeasured or unobserved) variables compositional Markov boundary methods may miss some Markov boundary members. The present invention circumvents this limitation of the compositional Markov boundary methods and proposes a new method that can discover Markov boundaries from the datasets with hidden variables and do so in a much more sample efficient manner than methods that directly implement the definition of the Markov boundary. In general, the inventive method transforms a dataset with many variables into a minimal reduced dataset where all variables are needed for optimal prediction of some response variable. The power of the invention was empirically demonstrated with data generated by Bayesian networks and with 13 real datasets from a diversity of application domains. | 08-18-2011 |
Peter Aliferis, Brampton CA
| Patent application number | Description | Published |
|---|---|---|
| 20090322473 | Remote controlled dead bolt door locking system - The remote controlled deadbolt door locking system (the “unit”) is designed to be an add-on safety mechanism for existing entry doors. The unit is designed to be mounted onto the bottom corner of a door, where in the engaged position, it prevents said door from being opened even if the main lock has been tampered with. The unit makes use of a small DC electric motor to move a steel shaft in the vertical direction into a steel bushing that is mounted into a drilled hole in the floor directly in front of the door and under the unit. The unit is mounted onto the door with four carriage bolts through the door, and a mounting plate from the outside of the door, through four matching holes in the unit itself. The unit is then simply tightened on with normal hex nuts. The unit is equipped with several safety circuits, which warn the user if any of the following occur: low battery or battery failure of either the main or backup batteries; the shaft does not fully engage upon closing; and if both batteries fall to a low condition. There is a built in triple redundancy to eliminate the possibility of the homeowner locking him/herself out. The operation of the unit is accomplished through a two-button remote control, or any commercially available remote entry system including but not limited to: fingerprint or voice recognition, or keypad entry. In this way, the unit acts just like a simple dead bolt, but one that can be locked while the homeowner is standing outside of the house. | 12-31-2009 |