LDA 2 definition
Examples of LDA 2 in a sentence
However, the generality of the foregoing notwithstanding, the CNDA and, if applicable, the LDA 2 executed by Publisher shall continue in full force and effect.
The entry in the ith row and jth column is the percentage of images from class i that were misidentified as class j.liance on latent Dirichlet allocation (LDA) [2], which is essentially an unsupervised dimensionality reduction tech- nique and as such, is not necessarily conducive to achiev- ing the highest classification accuracy.
A registered lobbyist, in turn, is a person listed in required filings as a lobbyist for a particular client by a registrant under the LDA, 2 U.S.C. § 1603(a), because of the person's actual or anticipated lobbying activities and contacts.
The other method was based on latent Dirichlet allocation (LDA) [2].
Nor does it prohibit former appointees from contacting "covered legislative branch officials," within the meaning of the LDA, 2 U.S.C. § 1602(4).
First, we use LDA [2], where each of K topics is represented by a term distribution φ, while each document has a topic distribution θ.
We apply Latent Dirichlet Allocation (LDA) [2] to cap- ture customers’ purchasing preferences, and evaluate our proposed predictive models based on a one-year group-purchasing dataset from ihergo.com.Our contributions in understanding the importance of social factors for group-deal customers’ decisions are the following: •A new type of group-purchasing dataset.
Indeed, while topic modelling approaches spe- cific to Twitter have been developed (e.g. Twitter LDA [2]), the suitability of these coherence metrics for Twitter data has not been tested.In this paper, we empirically investigate the appropriateness of ten auto- matic topic coherence metrics, by comparing how closely they align with human judgments of topic coherence.
Instead of imposing a hard cardinality constraint β 0 k, we may instead penalize by adding a penalty term1 λ β 0 or its natural relaxation, the l1 shrinkage used in Lasso [12], sparse LDA [2], and sparse PCA [14, 15].
Among existing approaches, Bayesian topic models, either para- metric (LDA [2], labeled-LDA [21]) or non-parametric (HDP [25]) have widely been widely applied due to the ability of mining la- tent topics hidden in tweet dataset [5, 8, 11, 14, 16, 18, 20, 22, 28].