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adatreenat

This software contains three closed tree mining algorithms: an incremental one IncTreeNat, a sliding-window based one, WinTreeNat, and finally one that mines closed trees adaptively from data streams, AdaTreeNat. Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information.

adwin

ADWIN is an adaptive sliding window algorithm for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters in learning and mining algorithms initially not designed for drifting data.

blstm-for-supervised-sequence-recognition

General Purpose Implementation of LSTM extended from OCROPUS to recognize any sequential data

cnn-lstm-ctc

An implementation of LSTM and CTC to recognize simple english sentence image

crystall-ball-event-prediction

Bigdata based event prediction using MapReduce algorithm

deep-semi-nmf

Theano-based implementation of Deep Semi-NMF.

dynamiclda

Dynamic Topic Modeling and Topic Chains of Reuters News Articles using SCVB0

endive

Rare event prediction library for Python

generalized-sequential-pattern-data-mining

Algorithms for mining sequential patterns based on the Minimum Support GSP (Global Sequential Pattern). Algorithm generates candidate keys at each level based on join step and prune step of previous level candidate keys.

graphnn

Training computational graph on top of structured data (string, graph, etc)

hazeremove

This project is the haze removal code

hinexpertfinding

Topic model for expert finding in heterogeneous information network

iir

Machine Learning / Natural Language Processing / Information Retrieval

incremental-svm-learning-in-matlab

This MATLAB package implements the methods for exact incremental/decremental SVM learning, regularization parameter perturbation and kernel parameter perturbation presented in "SVM Incremental Learning, Adaptation, and Optimization" by Christopher Diehl and Gert Cauwenberghs.

kam1n0-plugin-ida-pro

The Kam1n0 Assembly Clone Search Engine

netclus

Ranking-Based Clustering of Heterogeneous Information Networks With Star Network Schema NetClus Algorthms

nips2015_vrnn

nnformll

Neural Network Models for Multi-label learning

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

st-mrf

Real-time Video Dehazing based on Spatio-temporal MRF